task_classification stringclasses 5
values | task_abbr stringlengths 2 28 | question stringlengths 66 721 | reference_milestone_count int64 1 5 | reference_milestones listlengths 1 5 | reference_step_count int64 1 5 | reference_steps listlengths 1 5 | milestone_reference_files listlengths 0 9 | task_subclass stringclasses 17
values | dataset stringclasses 6
values | question_type stringclasses 2
values | test stringclasses 1
value | files listlengths 1 4 | order float64 11 52 | task stringclasses 17
values | index int64 1 327 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
O | GATK haplotyper | You are analyzing the genetic testing data of a patient with a genetic disease. The patient's variant data is stored in the following files: {MUT_edit1.fastq.gz}{MUT_edit2.fastq.gz}. Please use the GATK best practice workflow for variant identification, and based on the variant results, identify the pathogenic variants... | 3 | [
"Generate comparison result: bam",
"Generate VCF",
"There are records of mutation results: chr6-49427089-A-G, chr6-49419304-A-G."
] | 3 | [
"Compare and generate bam (additional steps can include: sort, dedup, gatk-bqsr).",
"Identification and generation of VCF",
"Comment, generate maf"
] | [
{
"name": "tumor.recal.bam",
"path": "downloaded_files\\index_0001\\milestone\\tumor.recal.bam"
},
{
"name": "tumor.recal.vcf",
"path": "downloaded_files\\index_0001\\milestone\\tumor.recal.vcf"
},
{
"name": "tumor.recal.fil.maf",
"path": "downloaded_files\\index_0001\\milestone\\tum... | DNA | Rep1 | clear | round3-IMF | [
{
"name": "MUT_edit1.fastq.gz",
"path": "downloaded_files\\index_0001\\files\\MUT_edit1.fastq.gz"
},
{
"name": "MUT_edit2.fastq.gz",
"path": "downloaded_files\\index_0001\\files\\MUT_edit2.fastq.gz"
}
] | 11.01 | DNA | 1 |
O | GATK haplotyper | You are analyzing the genetic testing data of a patient with a genetic disease. The patient's mutation data is stored in the following files: {MUT_edit1.fastq.gz}{MUT_edit2.fastq.gz}. You need to first identify the mutations, and based on the results of the mutations, please find the pathogenic mutations of the patient... | 3 | [
"Generate comparison result: bam",
"Generate VCF",
"There are records of mutation results: chr6-49427089-A-G, chr6-49419304-A-G."
] | 3 | [
"Compare and generate bam (additional steps can include: sort, dedup, gatk-bqsr).",
"Identification and generation of VCF",
"Comment, generate maf"
] | [
{
"name": "tumor.recal.bam",
"path": "downloaded_files\\index_0002\\milestone\\tumor.recal.bam"
},
{
"name": "tumor.recal.vcf",
"path": "downloaded_files\\index_0002\\milestone\\tumor.recal.vcf"
},
{
"name": "tumor.recal.fil.maf",
"path": "downloaded_files\\index_0002\\milestone\\tum... | DNA | Rep1 | open | round3-IMF | [
{
"name": "MUT_edit1.fastq.gz",
"path": "downloaded_files\\index_0002\\files\\MUT_edit1.fastq.gz"
},
{
"name": "MUT_edit2.fastq.gz",
"path": "downloaded_files\\index_0002\\files\\MUT_edit2.fastq.gz"
}
] | 11.02 | DNA | 2 |
O | GATK haplotyper | You are analyzing the genetic testing data of a patient with a genetic disease. The patient's variant data is stored in the following files: {PAH_edit1.fastq.gz}{PAH_edit2.fastq.gz}. Please use the GATK best practice workflow for variant identification, and based on the variant results, identify the pathogenic variants... | 3 | [
"Generate comparison result: bam",
"Generate VCF",
"There are records of mutation results: chr12-103246594-G-A, chr12-103246707-C-T."
] | 3 | [
"Compare, generate bam (additional steps can include: sort, dedup, gatk-bqsr)",
"Identification and generation of VCF",
"Comment, generate maf"
] | [
{
"name": "tumor.recal.bam",
"path": "downloaded_files\\index_0003\\milestone\\tumor.recal.bam"
},
{
"name": "tumor.recal.vcf",
"path": "downloaded_files\\index_0003\\milestone\\tumor.recal.vcf"
},
{
"name": "tumor.recal.fil.maf",
"path": "downloaded_files\\index_0003\\milestone\\tum... | DNA | Rep2 | clear | round3-IMF | [
{
"name": "PAH_edit1.fastq.gz",
"path": "downloaded_files\\index_0003\\files\\PAH_edit1.fastq.gz"
},
{
"name": "PAH_edit2.fastq.gz",
"path": "downloaded_files\\index_0003\\files\\PAH_edit2.fastq.gz"
}
] | 11.03 | DNA | 3 |
O | GATK haplotyper | {You are analyzing the genetic testing data of a patient with a genetic disease. The patient's variant data is stored in the following files: {PAH_edit1.fastq.gz} and {PAH_edit2.fastq.gz}. You need to first identify the variants, and based on the results of the variants, please find the pathogenic variants of the patie... | 3 | [
"Generate comparison result: bam",
"Generate VCF",
"There are records of mutation results: chr12-103246594-G-A, chr12-103246707-C-T."
] | 3 | [
"Compare and generate bam (additional steps can include: sort, dedup, gatk-bqsr).",
"Identification and generation of VCF",
"Comment, generate maf"
] | [
{
"name": "tumor.recal.bam",
"path": "downloaded_files\\index_0004\\milestone\\tumor.recal.bam"
},
{
"name": "tumor.recal.vcf",
"path": "downloaded_files\\index_0004\\milestone\\tumor.recal.vcf"
},
{
"name": "tumor.recal.fil.maf",
"path": "downloaded_files\\index_0004\\milestone\\tum... | DNA | Rep2 | open | round3-IMF | [
{
"name": "PAH_edit1.fastq.gz",
"path": "downloaded_files\\index_0004\\files\\PAH_edit1.fastq.gz"
},
{
"name": "PAH_edit2.fastq.gz",
"path": "downloaded_files\\index_0004\\files\\PAH_edit2.fastq.gz"
}
] | 11.04 | DNA | 4 |
O | GATK haplotyper | You are analyzing the genetic testing data of a patient with a genetic disease. The patient's variant data is stored in the following files: {SLC22A5_edit1.fastq.gz} and {SLC22A5_edit2.fastq.gz}. Please use the GATK best practice workflow for variant identification, and based on the variant results, identify the pathog... | 3 | [
"Generate comparison result: bam",
"Generate VCF",
"There are records of mutation results: chr5-131705701-T-G, chr5-131714095-A-G."
] | 3 | [
"Compare and generate bam (additional steps can include: sort, dedup, gatk-bqsr)",
"Identification and generation of VCF",
"Comment, generate maf"
] | [
{
"name": "tumor.recal.bam",
"path": "downloaded_files\\index_0005\\milestone\\tumor.recal.bam"
},
{
"name": "tumor.recal.vcf",
"path": "downloaded_files\\index_0005\\milestone\\tumor.recal.vcf"
},
{
"name": "tumor.recal.fil.maf",
"path": "downloaded_files\\index_0005\\milestone\\tum... | DNA | Rep3 | clear | round3-IMF | [
{
"name": "SLC22A5_edit1.fastq.gz",
"path": "downloaded_files\\index_0005\\files\\SLC22A5_edit1.fastq.gz"
},
{
"name": "SLC22A5_edit2.fastq.gz",
"path": "downloaded_files\\index_0005\\files\\SLC22A5_edit2.fastq.gz"
}
] | 11.05 | DNA | 5 |
O | GATK haplotyper | {You are analyzing the genetic testing data of a patient with a genetic disease. The patient's variant data is stored in the following files: {SLC22A5_edit1.fastq.gz}{SLC22A5_edit2.fastq.gz}. You need to first identify the variants, and based on the results of the variants, please find the pathogenic variants of the pa... | 3 | [
"Generate comparison result: bam",
"Generate VCF",
"There are records of mutation results: chr5-131705701-T-G, chr5-131714095-A-G."
] | 3 | [
"Compare and generate bam (additional steps can include: sort, dedup, gatk-bqsr).",
"Identification and generation of VCF",
"Comment, generate maf"
] | [
{
"name": "tumor.recal.bam",
"path": "downloaded_files\\index_0006\\milestone\\tumor.recal.bam"
},
{
"name": "tumor.recal.vcf",
"path": "downloaded_files\\index_0006\\milestone\\tumor.recal.vcf"
},
{
"name": "tumor.recal.fil.maf",
"path": "downloaded_files\\index_0006\\milestone\\tum... | DNA | Rep3 | open | round3-IMF | [
{
"name": "SLC22A5_edit1.fastq.gz",
"path": "downloaded_files\\index_0006\\files\\SLC22A5_edit1.fastq.gz"
},
{
"name": "SLC22A5_edit2.fastq.gz",
"path": "downloaded_files\\index_0006\\files\\SLC22A5_edit2.fastq.gz"
}
] | 11.06 | DNA | 6 |
O | Tumor-only somtatic SNVs | You are analyzing the genetic testing data of a tumor patient. The genomic variation data of this patient is stored in the following files: {BRAF_edit1.fastq.gz} and {BRAF_edit2.fastq.gz}. You need to use GATK to analyze and identify genomic variations and perform annotation of the variations. | 3 | [
"Generate comparison result: bam",
"Generate VCF",
"There is a record of the mutation result: chr7-140453136-T-A."
] | 3 | [
"Compare and generate bam (additional steps can include: sort, dedup, gatk-bqsr).",
"Identification and generation of VCF",
"Comment, generate maf"
] | [
{
"name": "tumor.recal.bam",
"path": "downloaded_files\\index_0007\\milestone\\tumor.recal.bam"
},
{
"name": "tumor.recal.vcf",
"path": "downloaded_files\\index_0007\\milestone\\tumor.recal.vcf"
},
{
"name": "tumor.recal.fil.maf",
"path": "downloaded_files\\index_0007\\milestone\\tum... | DNA | Rep1 | clear | round3-IMF | [
{
"name": "BRAF_edit1.fastq.gz",
"path": "downloaded_files\\index_0007\\files\\BRAF_edit1.fastq.gz"
},
{
"name": "BRAF_edit2.fastq.gz",
"path": "downloaded_files\\index_0007\\files\\BRAF_edit2.fastq.gz"
}
] | 11.07 | DNA | 7 |
O | Tumor-only somtatic SNVs | You are analyzing the genetic testing data of a tumor patient. The genomic variation data of this patient is stored in the following files: {BRAF_edit1.fastq.gz}{BRAF_edit2.fastq.gz}, identify genomic variations. | 3 | [
"Generate comparison result: bam",
"Generate VCF",
"There is a record of the mutation result: chr7-140453136-T-A."
] | 3 | [
"Compare and generate bam (additional steps can include: sort, dedup, gatk-bqsr).",
"Identification and generation of VCF",
"Comment, generate maf"
] | [
{
"name": "tumor.recal.bam",
"path": "downloaded_files\\index_0008\\milestone\\tumor.recal.bam"
},
{
"name": "tumor.recal.vcf",
"path": "downloaded_files\\index_0008\\milestone\\tumor.recal.vcf"
},
{
"name": "tumor.recal.fil.maf",
"path": "downloaded_files\\index_0008\\milestone\\tum... | DNA | Rep1 | open | round3-IMF | [
{
"name": "BRAF_edit1.fastq.gz",
"path": "downloaded_files\\index_0008\\files\\BRAF_edit1.fastq.gz"
},
{
"name": "BRAF_edit2.fastq.gz",
"path": "downloaded_files\\index_0008\\files\\BRAF_edit2.fastq.gz"
}
] | 11.08 | DNA | 8 |
O | Tumor-only somtatic SNVs | You are analyzing the genetic testing data of a tumor patient. The genomic variation data of this patient is stored in the following files: {EGFR_edit1.fastq.gz} {EGFR_edit2.fastq.gz}. You need to use GATK to analyze and identify genomic variations and perform variant annotation. | 3 | [
"generate bam",
"Generate VCF",
"There is a record of the mutation result: chr7-55259515-G-T."
] | 3 | [
"Compare and generate bam (additional steps can include: sort, dedup, gatk-bqsr).",
"Identification and generation of VCF",
"Comment, generate maf"
] | [
{
"name": "tumor.recal.bam",
"path": "downloaded_files\\index_0009\\milestone\\tumor.recal.bam"
},
{
"name": "tumor.recal.vcf",
"path": "downloaded_files\\index_0009\\milestone\\tumor.recal.vcf"
},
{
"name": "tumor.recal.fil.maf",
"path": "downloaded_files\\index_0009\\milestone\\tum... | DNA | Rep2 | clear | round3-IMF | [
{
"name": "EGFR_edit1.fastq.gz",
"path": "downloaded_files\\index_0009\\files\\EGFR_edit1.fastq.gz"
},
{
"name": "EGFR_edit2.fastq.gz",
"path": "downloaded_files\\index_0009\\files\\EGFR_edit2.fastq.gz"
}
] | 11.09 | DNA | 9 |
O | Tumor-only somtatic SNVs | You are analyzing the genetic testing data of a tumor patient. The genomic variation data of this patient is stored in the following files: {EGFR_edit1.fastq.gz} {EGFR_edit2.fastq.gz}, identifying genomic variations. | 3 | [
"generate bam",
"Generate VCF",
"There is a record of the mutation result: chr7-55259515-G-T."
] | 3 | [
"Compare and generate bam (additional steps can include: sort, dedup, gatk-bqsr).",
"Identification and generation of VCF",
"Comment, generate maf"
] | [
{
"name": "tumor.recal.bam",
"path": "downloaded_files\\index_0010\\milestone\\tumor.recal.bam"
},
{
"name": "tumor.recal.vcf",
"path": "downloaded_files\\index_0010\\milestone\\tumor.recal.vcf"
},
{
"name": "tumor.recal.fil.maf",
"path": "downloaded_files\\index_0010\\milestone\\tum... | DNA | Rep2 | open | round3-IMF | [
{
"name": "EGFR_edit1.fastq.gz",
"path": "downloaded_files\\index_0010\\files\\EGFR_edit1.fastq.gz"
},
{
"name": "EGFR_edit2.fastq.gz",
"path": "downloaded_files\\index_0010\\files\\EGFR_edit2.fastq.gz"
}
] | 11.1 | DNA | 10 |
O | Tumor-only somtatic SNVs | You are analyzing the genetic testing data of a tumor patient. The genomic variant data of this patient is stored in the following files: {KRAS_edit1.fastq.gz} {KRAS_edit2.fastq.gz}. You need to use GATK to analyze and identify genomic variants and perform variant annotation. | 3 | [
"generate bam",
"Generate VCF",
"There is a record of the mutation result: chr12-25398285-A-C."
] | 3 | [
"Compare and generate bam (additional steps can include: sort, dedup, gatk-bqsr).",
"Identification and generation of VCF",
"Comment, generate maf"
] | [
{
"name": "tumor.recal.bam",
"path": "downloaded_files\\index_0011\\milestone\\tumor.recal.bam"
},
{
"name": "tumor.recal.vcf",
"path": "downloaded_files\\index_0011\\milestone\\tumor.recal.vcf"
},
{
"name": "tumor.recal.fil.maf",
"path": "downloaded_files\\index_0011\\milestone\\tum... | DNA | Rep3 | clear | round3-IMF | [
{
"name": "KRAS_edit1.fastq.gz",
"path": "downloaded_files\\index_0011\\files\\KRAS_edit1.fastq.gz"
},
{
"name": "KRAS_edit2.fastq.gz",
"path": "downloaded_files\\index_0011\\files\\KRAS_edit2.fastq.gz"
}
] | 11.11 | DNA | 11 |
O | Tumor-only somtatic SNVs | You are analyzing the genetic testing data of a tumor patient. The genomic variation data of this patient is stored in the following files: {KRAS_edit1.fastq.gz} and {KRAS_edit2.fastq.gz}, identifying genomic variations. | 3 | [
"generate bam",
"Generate VCF",
"There is a record of the mutation result: chr12-25398285-A-C."
] | 3 | [
"Compare and generate bam (additional steps can include: sort, dedup, gatk-bqsr).",
"Identification and generation of VCF",
"Comment, generate maf"
] | [
{
"name": "tumor.recal.bam",
"path": "downloaded_files\\index_0012\\milestone\\tumor.recal.bam"
},
{
"name": "tumor.recal.vcf",
"path": "downloaded_files\\index_0012\\milestone\\tumor.recal.vcf"
},
{
"name": "tumor.recal.fil.maf",
"path": "downloaded_files\\index_0012\\milestone\\tum... | DNA | Rep3 | open | round3-IMF | [
{
"name": "KRAS_edit1.fastq.gz",
"path": "downloaded_files\\index_0012\\files\\KRAS_edit1.fastq.gz"
},
{
"name": "KRAS_edit2.fastq.gz",
"path": "downloaded_files\\index_0012\\files\\KRAS_edit2.fastq.gz"
}
] | 11.12 | DNA | 12 |
O | HaplotypeCaller | I have a genomic alignment BAM file {MUT_tumor.recal.bam}, using GATK Haplotype Caller to identify variants. | 2 | [
"Generate VCF",
"Generate maf, mutation results include records: chr6-49427089-A-G, chr6-49419304-A-G."
] | 2 | [
"Identification and generation of VCF",
"Comment, generate maf"
] | [
{
"name": "tumor.recal.vcf",
"path": "downloaded_files\\index_0013\\milestone\\tumor.recal.vcf"
},
{
"name": "tumor.recal.fil.maf",
"path": "downloaded_files\\index_0013\\milestone\\tumor.recal.fil.maf"
}
] | DNA | Rep1 | clear | round3-IMF | [
{
"name": "MUT_tumor.recal.bam",
"path": "downloaded_files\\index_0013\\files\\MUT_tumor.recal.bam"
}
] | 11.13 | DNA | 13 |
O | HaplotypeCaller | Perform variant identification on the bam file {MUT_tumor.recal.bam} | 2 | [
"Generate VCF",
"Generate maf, mutation results include records: chr6-49427089-A-G, chr6-49419304-A-G."
] | 2 | [
"Identification and generation of VCF",
"Comment, generate maf"
] | [
{
"name": "tumor.recal.vcf",
"path": "downloaded_files\\index_0014\\milestone\\tumor.recal.vcf"
},
{
"name": "tumor.recal.fil.maf",
"path": "downloaded_files\\index_0014\\milestone\\tumor.recal.fil.maf"
}
] | DNA | Rep1 | open | round3-IMF | [
{
"name": "MUT_tumor.recal.bam",
"path": "downloaded_files\\index_0014\\files\\MUT_tumor.recal.bam"
}
] | 11.14 | DNA | 14 |
O | HaplotypeCaller | I have a genomic alignment BAM file {PAH_tumor.recal.bam}, using GATK Haplotype Caller to identify variants. | 2 | [
"Generate VCF",
"Generate maf, mutation results include records: chr12-103246594-G-A, chr12-103246707-C-T."
] | 2 | [
"Identification and generation of VCF",
"Comment, generate maf"
] | [
{
"name": "tumor.recal.vcf",
"path": "downloaded_files\\index_0015\\milestone\\tumor.recal.vcf"
},
{
"name": "tumor.recal.fil.maf",
"path": "downloaded_files\\index_0015\\milestone\\tumor.recal.fil.maf"
}
] | DNA | Rep2 | clear | round3-IMF | [
{
"name": "PAH_tumor.recal.bam",
"path": "downloaded_files\\index_0015\\files\\PAH_tumor.recal.bam"
}
] | 11.15 | DNA | 15 |
O | HaplotypeCaller | Perform variant identification on the bam file {PAH_tumor.recal.bam} | 2 | [
"Generate VCF",
"Generate maf, mutation results include records: chr12-103246594-G-A, chr12-103246707-C-T."
] | 2 | [
"Identification and generation of VCF",
"Comment, generate maf"
] | [
{
"name": "tumor.recal.vcf",
"path": "downloaded_files\\index_0016\\milestone\\tumor.recal.vcf"
},
{
"name": "tumor.recal.fil.maf",
"path": "downloaded_files\\index_0016\\milestone\\tumor.recal.fil.maf"
}
] | DNA | Rep2 | open | round3-IMF | [
{
"name": "PAH_tumor.recal.bam",
"path": "downloaded_files\\index_0016\\files\\PAH_tumor.recal.bam"
}
] | 11.16 | DNA | 16 |
O | HaplotypeCaller | I have a genomic alignment BAM file {SLC22A5_tumor.recal.bam}, using GATK Haplotype Caller to identify variants. | 2 | [
"Generate VCF",
"Generate maf, mutation results include records: chr5-131705701-T-G, chr5-131714095-A-G."
] | 2 | [
"Identification and generation of VCF",
"Comment, generate maf"
] | [
{
"name": "tumor.recal.vcf",
"path": "downloaded_files\\index_0017\\milestone\\tumor.recal.vcf"
},
{
"name": "tumor.recal.fil.maf",
"path": "downloaded_files\\index_0017\\milestone\\tumor.recal.fil.maf"
}
] | DNA | Rep3 | clear | round3-IMF | [
{
"name": "SLC22A5_tumor.recal.bam",
"path": "downloaded_files\\index_0017\\files\\SLC22A5_tumor.recal.bam"
}
] | 11.17 | DNA | 17 |
O | HaplotypeCaller | Perform variant identification on the bam file {SLC22A5_tumor.recal.bam} | 2 | [
"Generate VCF",
"Generate maf, mutation results include records: chr5-131705701-T-G, chr5-131714095-A-G."
] | 2 | [
"Identification and generation of VCF",
"Comment, generate maf"
] | [
{
"name": "tumor.recal.vcf",
"path": "downloaded_files\\index_0018\\milestone\\tumor.recal.vcf"
},
{
"name": "tumor.recal.fil.maf",
"path": "downloaded_files\\index_0018\\milestone\\tumor.recal.fil.maf"
}
] | DNA | Rep3 | open | round3-IMF | [
{
"name": "SLC22A5_tumor.recal.bam",
"path": "downloaded_files\\index_0018\\files\\SLC22A5_tumor.recal.bam"
}
] | 11.18 | DNA | 18 |
O | Tumor-paired somatic SNVs | {You are analyzing the sequencing data of a tumor patient. The tumor tissue data of this patient is stored in the files {BRAF_edit1.fastq.gz} and {BRAF_edit2.fastq.gz}, while the data of the normal tissue is in {BRAF_normal1.fastq.gz} and {BRAF_normal2.fastq.gz}. First, align the fastq data of the tumor and normal tiss... | 4 | [
"Generate tumor bam",
"Generate contrast bam",
"Generate VCF",
"There is a record of the mutation result: chr7-140453136-T-A."
] | 4 | [
"Compare and generate a reference BAM (additional steps can include: sort, dedup, gatk-bqsr).",
"Compare and generate tumor BAM (additional steps can include: sort, dedup, gatk-bqsr).",
"Identification and generation of VCF",
"Comment, generate maf"
] | [
{
"name": "tumor.recal.bam",
"path": "downloaded_files\\index_0019\\milestone\\tumor.recal.bam"
},
{
"name": "normal.recal.bam",
"path": "downloaded_files\\index_0019\\milestone\\normal.recal.bam"
},
{
"name": "paired.vcf",
"path": "downloaded_files\\index_0019\\milestone\\paired.vcf... | DNA | Rep1 | clear | round3-IMF | [
{
"name": "BRAF_edit1.fastq.gz",
"path": "downloaded_files\\index_0019\\files\\BRAF_edit1.fastq.gz"
},
{
"name": "BRAF_edit2.fastq.gz",
"path": "downloaded_files\\index_0019\\files\\BRAF_edit2.fastq.gz"
},
{
"name": "BRAF_normal1.fastq.gz",
"path": "downloaded_files\\index_0019\\file... | 11.19 | DNA | 19 |
O | Tumor-paired somatic SNVs | You are analyzing the sequencing data of a tumor patient. The tumor tissue data for this patient is stored in the following files: {BRAF_edit1.fastq.gz} and {BRAF_edit2.fastq.gz}. The data for the normal tissue is in {BRAF_normal1.fastq.gz} and {BRAF_normal2.fastq.gz}. First, analyze the tumor and normal tissue to obta... | 4 | [
"Generate tumor bam",
"Generate contrast bam",
"Generate VCF",
"There is a record of the mutation result: chr7-140453136-T-A."
] | 4 | [
"Compare and generate a reference BAM (additional steps can include: sort, dedup, gatk-bqsr).",
"Compare and generate tumor BAM (additional steps can include: sort, dedup, gatk-bqsr).",
"Identification and generation of VCF",
"Comment, generate maf"
] | [
{
"name": "tumor.recal.bam",
"path": "downloaded_files\\index_0020\\milestone\\tumor.recal.bam"
},
{
"name": "normal.recal.bam",
"path": "downloaded_files\\index_0020\\milestone\\normal.recal.bam"
},
{
"name": "paired.vcf",
"path": "downloaded_files\\index_0020\\milestone\\paired.vcf... | DNA | Rep1 | open | round3-IMF | [
{
"name": "BRAF_edit1.fastq.gz",
"path": "downloaded_files\\index_0020\\files\\BRAF_edit1.fastq.gz"
},
{
"name": "BRAF_edit2.fastq.gz",
"path": "downloaded_files\\index_0020\\files\\BRAF_edit2.fastq.gz"
},
{
"name": "BRAF_normal1.fastq.gz",
"path": "downloaded_files\\index_0020\\file... | 11.2 | DNA | 20 |
O | Tumor-paired somatic SNVs | {You are analyzing the sequencing data of a tumor patient. The tumor tissue data of this patient is stored in the files {KRAS_edit1.fastq.gz} and {KRAS_edit2.fastq.gz}, while the data of the normal tissue is in {KRAS_normal1.fastq.gz} and {KRAS_normal2.fastq.gz}. First, use bwa to align the fastq data of the tumor and ... | 4 | [
"Generate tumor bam",
"Generate contrast bam",
"Generate VCF",
"There is a record of the mutation result: chr12-25398285-A-C."
] | 4 | [
"Compare and generate a reference BAM (additional steps can include: sort, dedup, gatk-bqsr).",
"Compare and generate tumor BAM (additional steps can include: sort, dedup, gatk-bqsr).",
"Identification and generation of VCF",
"Comment, generate maf"
] | [
{
"name": "tumor.recal.bam",
"path": "downloaded_files\\index_0021\\milestone\\tumor.recal.bam"
},
{
"name": "normal.recal.bam",
"path": "downloaded_files\\index_0021\\milestone\\normal.recal.bam"
},
{
"name": "paired.vcf",
"path": "downloaded_files\\index_0021\\milestone\\paired.vcf... | DNA | Rep2 | clear | round3-IMF | [
{
"name": "KRAS_edit1.fastq.gz",
"path": "downloaded_files\\index_0021\\files\\KRAS_edit1.fastq.gz"
},
{
"name": "KRAS_edit2.fastq.gz",
"path": "downloaded_files\\index_0021\\files\\KRAS_edit2.fastq.gz"
},
{
"name": "KRAS_normal1.fastq.gz",
"path": "downloaded_files\\index_0021\\file... | 11.21 | DNA | 21 |
O | Tumor-paired somatic SNVs | {You are analyzing the sequencing data of a tumor patient. The tumor tissue data for this patient is stored in the following files: {KRAS_edit1.fastq.gz}{KRAS_edit2.fastq.gz}, and the data for the normal tissue is in {KRAS_normal1.fastq.gz}{KRAS_normal2.fastq.gz}. First, analyze the tumor and normal tissue to obtain ba... | 4 | [
"Generate tumor bam",
"Generate contrast bam",
"Generate VCF",
"There is a record of the mutation result: chr12-25398285-A-C."
] | 4 | [
"Compare and generate a reference BAM (additional steps can include: sort, dedup, gatk-bqsr).",
"Compare and generate tumor BAM (additional steps can include: sort, dedup, gatk-bqsr).",
"Identification and generation of VCF",
"Comment, generate maf"
] | [
{
"name": "tumor.recal.bam",
"path": "downloaded_files\\index_0022\\milestone\\tumor.recal.bam"
},
{
"name": "normal.recal.bam",
"path": "downloaded_files\\index_0022\\milestone\\normal.recal.bam"
},
{
"name": "paired.vcf",
"path": "downloaded_files\\index_0022\\milestone\\paired.vcf... | DNA | Rep2 | open | round3-IMF | [
{
"name": "KRAS_edit1.fastq.gz",
"path": "downloaded_files\\index_0022\\files\\KRAS_edit1.fastq.gz"
},
{
"name": "KRAS_edit2.fastq.gz",
"path": "downloaded_files\\index_0022\\files\\KRAS_edit2.fastq.gz"
},
{
"name": "KRAS_normal1.fastq.gz",
"path": "downloaded_files\\index_0022\\file... | 11.22 | DNA | 22 |
O | Tumor-paired somatic SNVs | {You are analyzing the sequencing data of a tumor patient. The tumor tissue data of this patient is stored in the following files: {EGFR_edit1.fastq.gz} and {EGFR_edit2.fastq.gz}. The data of the normal tissue is in {EGFR_normal1.fastq.gz} and {EGFR_normal2.fastq.gz}. First, align the fastq data of the tumor and normal... | 4 | [
"Generate tumor bam",
"Generate contrast bam",
"Generate VCF",
"There is a record of the mutation result: chr7-55259515-G-T."
] | 4 | [
"Compare and generate a reference BAM (additional steps can include: sort, dedup, gatk-bqsr).",
"Compare and generate tumor BAM (additional steps can include: sort, dedup, gatk-bqsr).",
"Identification and generation of VCF",
"Comment, generate maf"
] | [
{
"name": "tumor.recal.bam",
"path": "downloaded_files\\index_0023\\milestone\\tumor.recal.bam"
},
{
"name": "normal.recal.bam",
"path": "downloaded_files\\index_0023\\milestone\\normal.recal.bam"
},
{
"name": "paired.vcf",
"path": "downloaded_files\\index_0023\\milestone\\paired.vcf... | DNA | Rep3 | clear | round3-IMF | [
{
"name": "EGFR_edit1.fastq.gz",
"path": "downloaded_files\\index_0023\\files\\EGFR_edit1.fastq.gz"
},
{
"name": "EGFR_edit2.fastq.gz",
"path": "downloaded_files\\index_0023\\files\\EGFR_edit2.fastq.gz"
},
{
"name": "EGFR_normal1.fastq.gz",
"path": "downloaded_files\\index_0023\\file... | 11.23 | DNA | 23 |
O | Tumor-paired somatic SNVs | You are analyzing the sequencing data of a tumor patient. The tumor tissue data of this patient is stored in the following files: {EGFR_edit1.fastq.gz}{EGFR_edit2.fastq.gz}, and the data of the normal tissue is in {EGFR_normal1.fastq.gz}{EGFR_normal2.fastq.gz}. First, analyze the tumor and normal tissue to obtain bam f... | 4 | [
"Generate tumor bam",
"Generate contrast bam",
"Generate VCF",
"There is a record of the mutation result: chr7-55259515-G-T."
] | 4 | [
"Compare and generate a reference BAM (additional steps can include: sort, dedup, gatk-bqsr).",
"Compare and generate tumor BAM (additional steps can include: sort, dedup, gatk-bqsr).",
"Identification and generation of VCF",
"Comment, generate maf"
] | [
{
"name": "tumor.recal.bam",
"path": "downloaded_files\\index_0024\\milestone\\tumor.recal.bam"
},
{
"name": "normal.recal.bam",
"path": "downloaded_files\\index_0024\\milestone\\normal.recal.bam"
},
{
"name": "paired.vcf",
"path": "downloaded_files\\index_0024\\milestone\\paired.vcf... | DNA | Rep3 | open | round3-IMF | [
{
"name": "EGFR_edit1.fastq.gz",
"path": "downloaded_files\\index_0024\\files\\EGFR_edit1.fastq.gz"
},
{
"name": "EGFR_edit2.fastq.gz",
"path": "downloaded_files\\index_0024\\files\\EGFR_edit2.fastq.gz"
},
{
"name": "EGFR_normal1.fastq.gz",
"path": "downloaded_files\\index_0024\\file... | 11.24 | DNA | 24 |
O | Mutect2 | I have a genomic alignment BAM file of tumor tissue {BRAF_tumor.recal.bam} and a genomic alignment BAM file of normal tissue {BRAF_normal.recal.bam}, using GATK's Mutect2 to identify variants. | 2 | [
"Generate VCF",
"Generate maf"
] | 2 | [
"Identification generates VCF",
"Comment, generate maf (optional)"
] | [
{
"name": "paired.vcf",
"path": "downloaded_files\\index_0025\\milestone\\paired.vcf"
},
{
"name": "paired.maf",
"path": "downloaded_files\\index_0025\\milestone\\paired.maf"
}
] | DNA | Rep1 | clear | round3-IMF | [
{
"name": "BRAF_normal.recal.bam",
"path": "downloaded_files\\index_0025\\files\\BRAF_normal.recal.bam"
},
{
"name": "BRAF_tumor.recal.bam",
"path": "downloaded_files\\index_0025\\files\\BRAF_tumor.recal.bam"
}
] | 11.25 | DNA | 25 |
O | Mutect2 | Use the bam file of tumor tissue {BRAF_tumor.recal.bam} and the bam file of normal tissue {BRAF_normal.recal.bam} for variant identification. | 2 | [
"Generate VCF",
"Generate maf"
] | 2 | [
"Identification and generation of VCF",
"Comment, generate maf (optional)"
] | [
{
"name": "paired.vcf",
"path": "downloaded_files\\index_0026\\milestone\\paired.vcf"
},
{
"name": "paired.maf",
"path": "downloaded_files\\index_0026\\milestone\\paired.maf"
}
] | DNA | Rep1 | open | round3-IMF | [
{
"name": "BRAF_normal.recal.bam",
"path": "downloaded_files\\index_0026\\files\\BRAF_normal.recal.bam"
},
{
"name": "BRAF_tumor.recal.bam",
"path": "downloaded_files\\index_0026\\files\\BRAF_tumor.recal.bam"
}
] | 11.26 | DNA | 26 |
O | Mutect2 | I have a genomic alignment BAM file of tumor tissue {EGFR_tumor.recal.bam} and a genomic alignment BAM file of normal tissue {EGFR_normal.recal.bam}, using GATK's mutect2 to identify variants. | 2 | [
"Generate VCF",
"Generate maf"
] | 2 | [
"Identification and generation of VCF",
"Comment, generate maf (optional)"
] | [
{
"name": "paired.vcf",
"path": "downloaded_files\\index_0027\\milestone\\paired.vcf"
},
{
"name": "paired.maf",
"path": "downloaded_files\\index_0027\\milestone\\paired.maf"
}
] | DNA | Rep2 | clear | round3-IMF | [
{
"name": "EGFR_normal.recal.bam",
"path": "downloaded_files\\index_0027\\files\\EGFR_normal.recal.bam"
},
{
"name": "EGFR_tumor.recal.bam",
"path": "downloaded_files\\index_0027\\files\\EGFR_tumor.recal.bam"
}
] | 11.27 | DNA | 27 |
O | Mutect2 | Use the bam file of tumor tissue {EGFR_tumor.recal.bam} and the bam file of normal tissue {EGFR_normal.recal.bam} for variant identification. | 2 | [
"Generate VCF",
"Generate maf"
] | 2 | [
"Identification and generation of VCF",
"Comment, generate maf (optional)"
] | [
{
"name": "paired.vcf",
"path": "downloaded_files\\index_0028\\milestone\\paired.vcf"
},
{
"name": "paired.maf",
"path": "downloaded_files\\index_0028\\milestone\\paired.maf"
}
] | DNA | Rep2 | open | round3-IMF | [
{
"name": "EGFR_normal.recal.bam",
"path": "downloaded_files\\index_0028\\files\\EGFR_normal.recal.bam"
},
{
"name": "EGFR_tumor.recal.bam",
"path": "downloaded_files\\index_0028\\files\\EGFR_tumor.recal.bam"
}
] | 11.28 | DNA | 28 |
O | Mutect2 | I have a genomic alignment BAM file of tumor tissue {KRAS_tumor.recal.bam} and a genomic alignment BAM file of normal tissue {KRAS_normal.recal.bam}, using GATK's mutect2 to identify variants. | 2 | [
"Generate VCF",
"Generate maf"
] | 2 | [
"Identification and generation of VCF",
"Comment, generate maf (optional)"
] | [
{
"name": "paired.vcf",
"path": "downloaded_files\\index_0029\\milestone\\paired.vcf"
},
{
"name": "paired.maf",
"path": "downloaded_files\\index_0029\\milestone\\paired.maf"
}
] | DNA | Rep3 | clear | round3-IMF | [
{
"name": "KRAS_normal.recal.bam",
"path": "downloaded_files\\index_0029\\files\\KRAS_normal.recal.bam"
},
{
"name": "KRAS_tumor.recal.bam",
"path": "downloaded_files\\index_0029\\files\\KRAS_tumor.recal.bam"
}
] | 11.29 | DNA | 29 |
O | Mutect2 | Use the bam file of tumor tissue {KRAS_tumor.recal.bam} and the bam file of normal tissue as a control {KRAS_normal.recal.bam} for variant identification. | 2 | [
"Generate VCF",
"Generate maf"
] | 2 | [
"Identification and generation of VCF",
"Comment, generate maf (optional)"
] | [
{
"name": "paired.vcf",
"path": "downloaded_files\\index_0030\\milestone\\paired.vcf"
},
{
"name": "paired.maf",
"path": "downloaded_files\\index_0030\\milestone\\paired.maf"
}
] | DNA | Rep3 | open | round3-IMF | [
{
"name": "KRAS_normal.recal.bam",
"path": "downloaded_files\\index_0030\\files\\KRAS_normal.recal.bam"
},
{
"name": "KRAS_tumor.recal.bam",
"path": "downloaded_files\\index_0030\\files\\KRAS_tumor.recal.bam"
}
] | 11.3 | DNA | 30 |
O | vcf annotation | I have a mutated vcf file {LJT-lung.pass.vcf}, and I want to use vcf2maf to perform variant annotation and generate a maf file. | 1 | [
"Generate maf"
] | 1 | [
"Comment, generate maf"
] | [
{
"name": "LJT-lung.pass.maf",
"path": "downloaded_files\\index_0031\\milestone\\LJT-lung.pass.maf"
}
] | DNA | Rep1 | clear | round3-IMF | [
{
"name": "LJT-lung.pass.vcf",
"path": "downloaded_files\\index_0031\\files\\LJT-lung.pass.vcf"
}
] | 11.31 | DNA | 31 |
O | vcf annotation | Perform variant annotation on the vcf file {LJT-lung.pass.vcf} to generate a maf file | 1 | [
"Generate maf"
] | 1 | [
"Comment, generate maf"
] | [
{
"name": "LJT-lung.pass.maf",
"path": "downloaded_files\\index_0032\\milestone\\LJT-lung.pass.maf"
}
] | DNA | Rep1 | open | round3-IMF | [
{
"name": "LJT-lung.pass.vcf",
"path": "downloaded_files\\index_0032\\files\\LJT-lung.pass.vcf"
}
] | 11.32 | DNA | 32 |
O | vcf annotation | I have a mutated vcf file {GJX-lung.pass.vcf}, using vcf2maf for variant annotation to generate a maf file. | 1 | [
"Generate maf"
] | 1 | [
"Comment, generate maf"
] | [
{
"name": "GJX-lung.pass.maf",
"path": "downloaded_files\\index_0033\\milestone\\GJX-lung.pass.maf"
}
] | DNA | Rep2 | clear | round3-IMF | [
{
"name": "GJX-lung.pass.vcf",
"path": "downloaded_files\\index_0033\\files\\GJX-lung.pass.vcf"
}
] | 11.33 | DNA | 33 |
O | vcf annotation | Perform variant annotation on the vcf file {GJX-lung.pass.vcf} to generate a maf file | 1 | [
"Generate maf"
] | 1 | [
"Comment, generate maf"
] | [
{
"name": "GJX-lung.pass.maf",
"path": "downloaded_files\\index_0034\\milestone\\GJX-lung.pass.maf"
}
] | DNA | Rep2 | open | round3-IMF | [
{
"name": "GJX-lung.pass.vcf",
"path": "downloaded_files\\index_0034\\files\\GJX-lung.pass.vcf"
}
] | 11.34 | DNA | 34 |
O | vcf annotation | I have a mutated vcf file {ZSH-lung.pass.vcf}, and I want to use vcf2maf to perform variant annotation and generate a maf file. | 1 | [
"Generate maf"
] | 1 | [
"Comment, generate maf"
] | [
{
"name": "ZSH-lung.pass.maf",
"path": "downloaded_files\\index_0035\\milestone\\ZSH-lung.pass.maf"
}
] | DNA | Rep3 | clear | round3-IMF | [
{
"name": "ZSH-lung.pass.vcf",
"path": "downloaded_files\\index_0035\\files\\ZSH-lung.pass.vcf"
}
] | 11.35 | DNA | 35 |
O | vcf annotation | Perform variant annotation on the VCF file {ZSH-lung.pass.vcf} to generate a MAF file | 1 | [
"Generate maf"
] | 1 | [
"Comment, generate maf"
] | [
{
"name": "ZSH-lung.pass.maf",
"path": "downloaded_files\\index_0036\\milestone\\ZSH-lung.pass.maf"
}
] | DNA | Rep3 | open | round3-IMF | [
{
"name": "ZSH-lung.pass.vcf",
"path": "downloaded_files\\index_0036\\files\\ZSH-lung.pass.vcf"
}
] | 11.36 | DNA | 36 |
O | DEG & enrichment | Currently, there is an expression profile data {exp_COAD.csv} and corresponding grouping information {group_COAD.csv}. Please use the DEG tool to calculate differential genes and use the KOBAS_enrichment tool to perform gene set functional enrichment analysis on the differential genes. | 2 | [
"Generate differential genes",
"Generate functional enrichment results"
] | 2 | [
"Calculate differential genes",
"Gene Set Functional Enrichment Analysis"
] | [
{
"name": "DEG_COAD.csv",
"path": "downloaded_files\\index_0037\\milestone\\DEG_COAD.csv"
},
{
"name": "KEGG.tsv",
"path": "downloaded_files\\index_0037\\milestone\\KEGG.tsv"
},
{
"name": "GO.tsv",
"path": "downloaded_files\\index_0037\\milestone\\GO.tsv"
}
] | RNA | Rep1 | clear | round3-IMF | [
{
"name": "exp_COAD.csv",
"path": "downloaded_files\\index_0037\\files\\exp_COAD.csv"
},
{
"name": "group_COAD.csv",
"path": "downloaded_files\\index_0037\\files\\group_COAD.csv"
}
] | 12.01 | RNA | 37 |
O | DEG & enrichment | Currently, there is an expression profile data {exp_COAD.csv} and corresponding grouping information {group_COAD.csv}. Calculate the gene set functional enrichment results for the differentially expressed genes between the two groups. | 2 | [
"Generate differential genes",
"Generate functional enrichment results"
] | 2 | [
"Calculate differential genes",
"Gene Set Functional Enrichment Analysis"
] | [
{
"name": "DEG_COAD.csv",
"path": "downloaded_files\\index_0038\\milestone\\DEG_COAD.csv"
},
{
"name": "GSEA_COAD.tsv",
"path": "downloaded_files\\index_0038\\milestone\\GSEA_COAD.tsv"
}
] | RNA | Rep1 | open | round3-IMF | [
{
"name": "exp_COAD.csv",
"path": "downloaded_files\\index_0038\\files\\exp_COAD.csv"
},
{
"name": "group_COAD.csv",
"path": "downloaded_files\\index_0038\\files\\group_COAD.csv"
}
] | 12.02 | RNA | 38 |
O | DEG & enrichment | {Currently, there is an expression profile data {exp_ORAD.csv}, corresponding grouping information {group_ORAD.csv}. Please use the DEG tool to calculate differential genes and use the KOBAS_enrichment tool to perform gene set functional enrichment analysis on the differential genes.} | 2 | [
"Generate differential genes",
"Generate functional enrichment results"
] | 2 | [
"Calculate differential genes",
"Gene Set Functional Enrichment Analysis"
] | [
{
"name": "DEG_ORAD.csv",
"path": "downloaded_files\\index_0039\\milestone\\DEG_ORAD.csv"
},
{
"name": "KEGG.tsv",
"path": "downloaded_files\\index_0039\\milestone\\KEGG.tsv"
},
{
"name": "GO.tsv",
"path": "downloaded_files\\index_0039\\milestone\\GO.tsv"
}
] | RNA | Rep2 | clear | round3-IMF | [
{
"name": "exp_ORAD.csv",
"path": "downloaded_files\\index_0039\\files\\exp_ORAD.csv"
},
{
"name": "group_ORAD.csv",
"path": "downloaded_files\\index_0039\\files\\group_ORAD.csv"
}
] | 12.03 | RNA | 39 |
O | DEG & enrichment | Currently, there is an expression profile data {exp_ORAD.csv} and corresponding grouping information {group_ORAD.csv}. Calculate the gene set functional enrichment results of differentially expressed genes between the two groups. | 2 | [
"Generate differential genes",
"Generate GSEA enrichment results"
] | 2 | [
"Calculate differential genes",
"Functional enrichment analysis based on differences"
] | [
{
"name": "DEG_ORAD.csv",
"path": "downloaded_files\\index_0040\\milestone\\DEG_ORAD.csv"
},
{
"name": "GSEA_ORAD.tsv",
"path": "downloaded_files\\index_0040\\milestone\\GSEA_ORAD.tsv"
}
] | RNA | Rep2 | open | round3-IMF | [
{
"name": "exp_ORAD.csv",
"path": "downloaded_files\\index_0040\\files\\exp_ORAD.csv"
},
{
"name": "group_ORAD.csv",
"path": "downloaded_files\\index_0040\\files\\group_ORAD.csv"
}
] | 12.04 | RNA | 40 |
O | DEG & enrichment | {Currently, there is an expression profile data {exp_READ.csv} and corresponding grouping information {group_READ.csv}. Please use the DEG tool to calculate differential genes and use the KOBAS_enrichment tool to perform gene set functional enrichment analysis on the differential genes.} | 2 | [
"Generate differential genes",
"Generate functional enrichment results"
] | 2 | [
"Calculate differential genes",
"Gene Set Functional Enrichment Analysis"
] | [
{
"name": "DEG_READ.csv",
"path": "downloaded_files\\index_0041\\milestone\\DEG_READ.csv"
},
{
"name": "KEGG.tsv",
"path": "downloaded_files\\index_0041\\milestone\\KEGG.tsv"
},
{
"name": "GO.tsv",
"path": "downloaded_files\\index_0041\\milestone\\GO.tsv"
}
] | RNA | Rep3 | clear | round3-IMF | [
{
"name": "exp_READ.csv",
"path": "downloaded_files\\index_0041\\files\\exp_READ.csv"
},
{
"name": "group_READ.csv",
"path": "downloaded_files\\index_0041\\files\\group_READ.csv"
}
] | 12.05 | RNA | 41 |
O | DEG & enrichment | Currently, there is an expression profile data {exp_READ.csv} and corresponding grouping information {group_READ.csv}. Calculate the gene set functional enrichment results of differentially expressed genes between the two groups. | 2 | [
"Generate differential genes",
"Generate GSEA enrichment results"
] | 2 | [
"Calculate differential genes",
"Functional enrichment analysis based on differential genes"
] | [
{
"name": "DEG_READ.csv",
"path": "downloaded_files\\index_0042\\milestone\\DEG_READ.csv"
},
{
"name": "GSEA_READ.tsv",
"path": "downloaded_files\\index_0042\\milestone\\GSEA_READ.tsv"
}
] | RNA | Rep3 | open | round3-IMF | [
{
"name": "exp_READ.csv",
"path": "downloaded_files\\index_0042\\files\\exp_READ.csv"
},
{
"name": "group_READ.csv",
"path": "downloaded_files\\index_0042\\files\\group_READ.csv"
}
] | 12.06 | RNA | 42 |
O | WGCNA | Currently, there is a standardized expression profile data {exp_COAD.csv} and preprocessed grouping information {group_COAD.csv}. Please use the WGCNA tool to select the genes most related to the grouping information and use the KOBAS_enrichment tool for gene set functional enrichment analysis. | 2 | [
"Generate a list of related genes",
"Generate functional enrichment results"
] | 2 | [
"Perform WGCNA",
"Gene Set Functional Enrichment Analysis"
] | [
{
"name": "top_hub_gene.csv",
"path": "downloaded_files\\index_0043\\milestone\\top_hub_gene.csv"
},
{
"name": "KEGG.tsv",
"path": "downloaded_files\\index_0043\\milestone\\KEGG.tsv"
},
{
"name": "GO.tsv",
"path": "downloaded_files\\index_0043\\milestone\\GO.tsv"
}
] | RNA | Rep1 | clear | round3-IMF | [
{
"name": "exp_COAD.csv",
"path": "downloaded_files\\index_0043\\files\\exp_COAD.csv"
},
{
"name": "group_COAD.csv",
"path": "downloaded_files\\index_0043\\files\\group_COAD.csv"
}
] | 12.07 | RNA | 43 |
O | WGCNA | Currently, there is a standardized expression profile data {exp_COAD.csv} and preprocessed grouping information {group_COAD.csv}. Please select the genes most relevant to the grouping information and perform gene set functional enrichment analysis. | 2 | [
"Generate differential genes",
"Generate functional enrichment results"
] | 2 | [
"Calculate differential genes",
"Gene Set Functional Enrichment Analysis"
] | [
{
"name": "DEG_COAD.csv",
"path": "downloaded_files\\index_0044\\milestone\\DEG_COAD.csv"
},
{
"name": "KEGG.tsv",
"path": "downloaded_files\\index_0044\\milestone\\KEGG.tsv"
},
{
"name": "GO.tsv",
"path": "downloaded_files\\index_0044\\milestone\\GO.tsv"
}
] | RNA | Rep1 | open | round3-IMF | [
{
"name": "exp_COAD.csv",
"path": "downloaded_files\\index_0044\\files\\exp_COAD.csv"
},
{
"name": "group_COAD.csv",
"path": "downloaded_files\\index_0044\\files\\group_COAD.csv"
}
] | 12.08 | RNA | 44 |
O | WGCNA | Currently, there is a standardized expression profile data {exp_ORAD.csv} and preprocessed grouping information {group_ORAD.csv}. Please use the WGCNA tool to select the genes most related to the grouping information and use the KOBAS_enrichment tool for gene set functional enrichment analysis. | 2 | [
"Generate a list of related genes",
"Generate functional enrichment results"
] | 2 | [
"Perform WGCNA",
"Gene Set Functional Enrichment Analysis"
] | [
{
"name": "top_hub_gene.csv",
"path": "downloaded_files\\index_0045\\milestone\\top_hub_gene.csv"
},
{
"name": "KEGG.tsv",
"path": "downloaded_files\\index_0045\\milestone\\KEGG.tsv"
},
{
"name": "GO.tsv",
"path": "downloaded_files\\index_0045\\milestone\\GO.tsv"
}
] | RNA | Rep2 | clear | round3-IMF | [
{
"name": "exp_ORAD.csv",
"path": "downloaded_files\\index_0045\\files\\exp_ORAD.csv"
},
{
"name": "group_ORAD.csv",
"path": "downloaded_files\\index_0045\\files\\group_ORAD.csv"
}
] | 12.09 | RNA | 45 |
O | WGCNA | Currently, there is a standardized expression profile data {exp_ORAD.csv} and preprocessed grouping information {group_ORAD.csv}. Please select the genes most relevant to the grouping information and perform gene set functional enrichment analysis. | 2 | [
"Generate differential genes",
"Generate functional enrichment results"
] | 2 | [
"Calculate differential genes",
"Gene Set Functional Enrichment Analysis"
] | [
{
"name": "DEG_ORAD.csv",
"path": "downloaded_files\\index_0046\\milestone\\DEG_ORAD.csv"
},
{
"name": "KEGG.tsv",
"path": "downloaded_files\\index_0046\\milestone\\KEGG.tsv"
},
{
"name": "GO.tsv",
"path": "downloaded_files\\index_0046\\milestone\\GO.tsv"
}
] | RNA | Rep2 | open | round3-IMF | [
{
"name": "exp_ORAD.csv",
"path": "downloaded_files\\index_0046\\files\\exp_ORAD.csv"
},
{
"name": "group_ORAD.csv",
"path": "downloaded_files\\index_0046\\files\\group_ORAD.csv"
}
] | 12.1 | RNA | 46 |
O | WGCNA | Currently, there is a standardized expression profile data {exp_READ.csv} and preprocessed grouping information {group_READ.csv}. Please use the WGCNA tool to select genes most related to the grouping information and use the KOBAS_enrichment tool for gene set functional enrichment analysis. | 2 | [
"Generate a list of related genes",
"Generate functional enrichment results"
] | 2 | [
"Perform WGCNA",
"Gene Set Functional Enrichment Analysis"
] | [
{
"name": "top_hub_gene.csv",
"path": "downloaded_files\\index_0047\\milestone\\top_hub_gene.csv"
},
{
"name": "KEGG.tsv",
"path": "downloaded_files\\index_0047\\milestone\\KEGG.tsv"
},
{
"name": "GO.tsv",
"path": "downloaded_files\\index_0047\\milestone\\GO.tsv"
}
] | RNA | Rep3 | clear | round3-IMF | [
{
"name": "exp_READ.csv",
"path": "downloaded_files\\index_0047\\files\\exp_READ.csv"
},
{
"name": "group_READ.csv",
"path": "downloaded_files\\index_0047\\files\\group_READ.csv"
}
] | 12.11 | RNA | 47 |
O | WGCNA | Currently, there is a standardized expression profile data {exp_READ.csv} and preprocessed grouping information {group_READ.csv}. Please select the genes most relevant to the grouping information and perform gene set functional enrichment analysis. | 2 | [
"Generate differential genes",
"Generate functional enrichment results"
] | 2 | [
"Calculate differential genes",
"Gene Set Functional Enrichment Analysis"
] | [
{
"name": "DEG_READ.csv",
"path": "downloaded_files\\index_0048\\milestone\\DEG_READ.csv"
},
{
"name": "KEGG.tsv",
"path": "downloaded_files\\index_0048\\milestone\\KEGG.tsv"
},
{
"name": "GO.tsv",
"path": "downloaded_files\\index_0048\\milestone\\GO.tsv"
}
] | RNA | Rep3 | open | round3-IMF | [
{
"name": "exp_READ.csv",
"path": "downloaded_files\\index_0048\\files\\exp_READ.csv"
},
{
"name": "group_READ.csv",
"path": "downloaded_files\\index_0048\\files\\group_READ.csv"
}
] | 12.12 | RNA | 48 |
O | KOBAS | {I have a gene list {PI3K-Akt.csv}, using KOBAS_enrichment for gene set functional enrichment} | 1 | [
"Generate enrichment result file"
] | 1 | [
"Utilizing KOBAS_enrichment for gene set functional enrichment"
] | [
{
"name": "KEGG.tsv",
"path": "downloaded_files\\index_0049\\milestone\\KEGG.tsv"
},
{
"name": "GO.tsv",
"path": "downloaded_files\\index_0049\\milestone\\GO.tsv"
}
] | RNA | Rep1 | clear | round3-IMF | [
{
"name": "PI3K-Akt.csv",
"path": "downloaded_files\\index_0049\\files\\PI3K-Akt.csv"
}
] | 12.13 | RNA | 49 |
O | KOBAS | Perform gene set functional enrichment on the gene list {TGF_signaling.csv} | 1 | [
"Generate functional enrichment result file"
] | 1 | [
"Gene set functional enrichment"
] | [
{
"name": "KEGG.tsv",
"path": "downloaded_files\\index_0050\\milestone\\KEGG.tsv"
},
{
"name": "GO.tsv",
"path": "downloaded_files\\index_0050\\milestone\\GO.tsv"
}
] | RNA | Rep1 | open | round3-IMF | [
{
"name": "TGF_signaling.csv",
"path": "downloaded_files\\index_0050\\files\\TGF_signaling.csv"
}
] | 12.14 | RNA | 50 |
O | KOBAS | I have a gene list {TGF_signaling.csv}, using KOBAS_enrichment for gene set functional enrichment | 1 | [
"Generate functional enrichment result file"
] | 1 | [
"Gene set functional enrichment"
] | [
{
"name": "KEGG.tsv",
"path": "downloaded_files\\index_0051\\milestone\\KEGG.tsv"
},
{
"name": "GO.tsv",
"path": "downloaded_files\\index_0051\\milestone\\GO.tsv"
}
] | RNA | Rep2 | clear | round3-IMF | [
{
"name": "TGF_signaling.csv",
"path": "downloaded_files\\index_0051\\files\\TGF_signaling.csv"
}
] | 12.15 | RNA | 51 |
O | KOBAS | Perform gene set functional enrichment on the gene list {PI3K-Akt.csv} | 1 | [
"Generate functional enrichment result file"
] | 1 | [
"Gene set functional enrichment"
] | [
{
"name": "KEGG.tsv",
"path": "downloaded_files\\index_0052\\milestone\\KEGG.tsv"
},
{
"name": "GO.tsv",
"path": "downloaded_files\\index_0052\\milestone\\GO.tsv"
}
] | RNA | Rep2 | open | round3-IMF | [
{
"name": "PI3K-Akt.csv",
"path": "downloaded_files\\index_0052\\files\\PI3K-Akt.csv"
}
] | 12.16 | RNA | 52 |
O | KOBAS | {I have a gene list {VGFR.csv}, using KOBAS_enrichment for gene set functional enrichment} | 1 | [
"Generate functional enrichment result file"
] | 1 | [
"Gene set functional enrichment"
] | [
{
"name": "KEGG.tsv",
"path": "downloaded_files\\index_0053\\milestone\\KEGG.tsv"
},
{
"name": "GO.tsv",
"path": "downloaded_files\\index_0053\\milestone\\GO.tsv"
}
] | RNA | Rep3 | clear | round3-IMF | [
{
"name": "VGFR.csv",
"path": "downloaded_files\\index_0053\\files\\VGFR.csv"
}
] | 12.17 | RNA | 53 |
O | KOBAS | Perform gene set functional enrichment on the gene list {VGFR.csv} | 1 | [
"Generate functional enrichment result file"
] | 1 | [
"Gene set functional enrichment"
] | [
{
"name": "KEGG.tsv",
"path": "downloaded_files\\index_0054\\milestone\\KEGG.tsv"
},
{
"name": "GO.tsv",
"path": "downloaded_files\\index_0054\\milestone\\GO.tsv"
}
] | RNA | Rep3 | open | round3-IMF | [
{
"name": "VGFR.csv",
"path": "downloaded_files\\index_0054\\files\\VGFR.csv"
}
] | 12.18 | RNA | 54 |
O | MCPCounter | Quantitative analysis of immune cells using MCPCounter on the expression profile file {exp_COAD.tsv} | 2 | [
"Generate quantitative results of immune cells",
"Check the quantitative results of immune cells."
] | 2 | [
"Generate quantitative results of immune cells",
"Check the quantitative results of immune cells (optional)"
] | [
{
"name": "McpCounter.tsv",
"path": "downloaded_files\\index_0055\\milestone\\McpCounter.tsv"
},
{
"name": "McpCounter.tsv",
"path": "downloaded_files\\index_0055\\milestone\\McpCounter.tsv"
}
] | RNA | Rep1 | clear | round3-IMF | [
{
"name": "exp_COAD.tsv",
"path": "downloaded_files\\index_0055\\files\\exp_COAD.tsv"
}
] | 12.19 | RNA | 55 |
O | MCPCounter | Perform immune cell quantification analysis on the expression profile file {exp_COAD.tsv} | 2 | [
"Generate quantitative results of immune cells",
"Check the quantitative results of immune cells."
] | 2 | [
"Generate quantitative results of immune cells",
"Check the quantitative results of immune cells (optional)"
] | [
{
"name": "McpCounter.tsv",
"path": "downloaded_files\\index_0056\\milestone\\McpCounter.tsv"
},
{
"name": "McpCounter.tsv",
"path": "downloaded_files\\index_0056\\milestone\\McpCounter.tsv"
}
] | RNA | Rep1 | open | round3-IMF | [
{
"name": "exp_COAD.tsv",
"path": "downloaded_files\\index_0056\\files\\exp_COAD.tsv"
}
] | 12.2 | RNA | 56 |
O | MCPCounter | Quantitative analysis of immune cells using MCPCounter on the expression profile file {exp_ORAD.tsv} | 2 | [
"Generate quantitative results of immune cells",
"Check the quantitative results of immune cells."
] | 2 | [
"Generate quantitative results of immune cells",
"Check the quantitative results of immune cells (optional)"
] | [
{
"name": "McpCounter.tsv",
"path": "downloaded_files\\index_0057\\milestone\\McpCounter.tsv"
},
{
"name": "McpCounter.tsv",
"path": "downloaded_files\\index_0057\\milestone\\McpCounter.tsv"
}
] | RNA | Rep2 | clear | round3-IMF | [
{
"name": "exp_ORAD.tsv",
"path": "downloaded_files\\index_0057\\files\\exp_ORAD.tsv"
}
] | 12.21 | RNA | 57 |
O | MCPCounter | Perform immune cell quantification analysis on the expression profile file {exp_ORAD.tsv} | 2 | [
"Generate quantitative results of immune cells",
"Check the quantitative results of immune cells."
] | 2 | [
"Generate quantitative results of immune cells",
"Check the quantitative results of immune cells (optional)"
] | [
{
"name": "McpCounter.tsv",
"path": "downloaded_files\\index_0058\\milestone\\McpCounter.tsv"
},
{
"name": "McpCounter.tsv",
"path": "downloaded_files\\index_0058\\milestone\\McpCounter.tsv"
}
] | RNA | Rep2 | open | round3-IMF | [
{
"name": "exp_ORAD.tsv",
"path": "downloaded_files\\index_0058\\files\\exp_ORAD.tsv"
}
] | 12.22 | RNA | 58 |
O | MCPCounter | Quantitative analysis of immune cells using MCPCounter on the expression profile file {exp_READ.tsv} | 2 | [
"Generate quantitative results of immune cells",
"Check the quantitative results of immune cells (optional)"
] | 2 | [
"Generate quantitative results of immune cells",
"Check the quantitative results of immune cells (optional)"
] | [
{
"name": "McpCounter.tsv",
"path": "downloaded_files\\index_0059\\milestone\\McpCounter.tsv"
},
{
"name": "McpCounter.tsv",
"path": "downloaded_files\\index_0059\\milestone\\McpCounter.tsv"
}
] | RNA | Rep3 | clear | round3-IMF | [
{
"name": "exp_READ.tsv",
"path": "downloaded_files\\index_0059\\files\\exp_READ.tsv"
}
] | 12.23 | RNA | 59 |
O | MCPCounter | Perform immune cell quantification analysis on the expression profile file {exp_READ.tsv} | 2 | [
"Generate quantitative results of immune cells",
"Check the quantitative results of immune cells."
] | 2 | [
"Generate quantitative results of immune cells",
"Check the quantitative results of immune cells (optional)"
] | [
{
"name": "McpCounter.tsv",
"path": "downloaded_files\\index_0060\\milestone\\McpCounter.tsv"
},
{
"name": "McpCounter.tsv",
"path": "downloaded_files\\index_0060\\milestone\\McpCounter.tsv"
}
] | RNA | Rep3 | open | round3-IMF | [
{
"name": "exp_READ.tsv",
"path": "downloaded_files\\index_0060\\files\\exp_READ.tsv"
}
] | 12.24 | RNA | 60 |
O | DEG | {Expression profile data {exp_COAD.csv}, corresponding grouping information {group_COAD.csv}, please use DEG tools to calculate differential genes} | 1 | [
"Generate differential genes"
] | 1 | [
"Calculate differential genes"
] | [
{
"name": "DEG_COAD.csv",
"path": "downloaded_files\\index_0061\\milestone\\DEG_COAD.csv"
}
] | RNA | Rep1 | clear | round3-IMF | [
{
"name": "exp_COAD.csv",
"path": "downloaded_files\\index_0061\\files\\exp_COAD.csv"
},
{
"name": "group_COAD.csv",
"path": "downloaded_files\\index_0061\\files\\group_COAD.csv"
}
] | 12.25 | RNA | 61 |
O | DEG | {Expression profile data {exp_COAD.csv}, corresponding grouping information {group_COAD.csv}, calculate the differential genes between groups} | 1 | [
"Generate differential genes"
] | 1 | [
"Calculate differential genes"
] | [
{
"name": "DEG_COAD.csv",
"path": "downloaded_files\\index_0062\\milestone\\DEG_COAD.csv"
}
] | RNA | Rep1 | open | round3-IMF | [
{
"name": "exp_COAD.csv",
"path": "downloaded_files\\index_0062\\files\\exp_COAD.csv"
},
{
"name": "group_COAD.csv",
"path": "downloaded_files\\index_0062\\files\\group_COAD.csv"
}
] | 12.26 | RNA | 62 |
O | DEG | {Expression profile data {exp_READ.csv}, corresponding grouping information {group_READ.csv}, please use DEG tools to calculate differential genes} | 1 | [
"Generate differential genes"
] | 1 | [
"Calculate differential genes"
] | [
{
"name": "DEG_READ.csv",
"path": "downloaded_files\\index_0063\\milestone\\DEG_READ.csv"
}
] | RNA | Rep2 | clear | round3-IMF | [
{
"name": "exp_READ.csv",
"path": "downloaded_files\\index_0063\\files\\exp_READ.csv"
},
{
"name": "group_READ.csv",
"path": "downloaded_files\\index_0063\\files\\group_READ.csv"
}
] | 12.27 | RNA | 63 |
O | DEG | {Expression profile data {exp_READ.csv}, corresponding grouping information {group_READ.csv}, calculate the differential genes between groups} | 1 | [
"Generate differential genes"
] | 1 | [
"Calculate differential genes"
] | [
{
"name": "DEG_READ.csv",
"path": "downloaded_files\\index_0064\\milestone\\DEG_READ.csv"
}
] | RNA | Rep2 | open | round3-IMF | [
{
"name": "exp_READ.csv",
"path": "downloaded_files\\index_0064\\files\\exp_READ.csv"
},
{
"name": "group_READ.csv",
"path": "downloaded_files\\index_0064\\files\\group_READ.csv"
}
] | 12.28 | RNA | 64 |
O | DEG | {Expression profile data {exp_ORAD.csv}, corresponding grouping information {group_ORAD.csv}, please use DEG tools to calculate differential genes} | 1 | [
"Generate differential genes"
] | 1 | [
"Calculate differential genes"
] | [
{
"name": "DEG_ORAD.csv",
"path": "downloaded_files\\index_0065\\milestone\\DEG_ORAD.csv"
}
] | RNA | Rep3 | clear | round3-IMF | [
{
"name": "exp_ORAD.csv",
"path": "downloaded_files\\index_0065\\files\\exp_ORAD.csv"
},
{
"name": "group_ORAD.csv",
"path": "downloaded_files\\index_0065\\files\\group_ORAD.csv"
}
] | 12.29 | RNA | 65 |
O | DEG | {Expression profile data {exp_ORAD.csv}, corresponding grouping information {group_ORAD.csv}, calculate the differential genes between groups} | 1 | [
"Generate differential genes"
] | 1 | [
"Calculate differential genes"
] | [
{
"name": "DEG_ORAD.csv",
"path": "downloaded_files\\index_0066\\milestone\\DEG_ORAD.csv"
}
] | RNA | Rep3 | open | round3-IMF | [
{
"name": "exp_ORAD.csv",
"path": "downloaded_files\\index_0066\\files\\exp_ORAD.csv"
},
{
"name": "group_ORAD.csv",
"path": "downloaded_files\\index_0066\\files\\group_ORAD.csv"
}
] | 12.3 | RNA | 66 |
O | Kallisto | {For RNA-seq sequencing data of individuals {reads_1.fastq.gz} and {reads_2.fastq.gz}, use kallisto to calculate the expression profile} | 1 | [
"Generate expression results"
] | 1 | [
"Calculate expression levels and generate expression files."
] | [
{
"name": "expression.txt",
"path": "downloaded_files\\index_0067\\milestone\\expression.txt"
}
] | RNA | Rep1 | clear | round3-IMF | [
{
"name": "reads_1.fastq.gz",
"path": "downloaded_files\\index_0067\\files\\reads_1.fastq.gz"
},
{
"name": "reads_2.fastq.gz",
"path": "downloaded_files\\index_0067\\files\\reads_2.fastq.gz"
}
] | 12.31 | RNA | 67 |
O | Kallisto | Calculate the expression profile for RNA-seq sequencing data of individuals {reads_1.fastq.gz} and {reads_2.fastq.gz} | 1 | [
"Generate expression results"
] | 1 | [
"Calculate expression levels and generate expression files."
] | [
{
"name": "expression.txt",
"path": "downloaded_files\\index_0068\\milestone\\expression.txt"
}
] | RNA | Rep1 | open | round3-IMF | [
{
"name": "reads_1.fastq.gz",
"path": "downloaded_files\\index_0068\\files\\reads_1.fastq.gz"
},
{
"name": "reads_2.fastq.gz",
"path": "downloaded_files\\index_0068\\files\\reads_2.fastq.gz"
}
] | 12.32 | RNA | 68 |
O | Kallisto | Using RNA-seq sequencing data for individuals {A22153A1_1.clean.fq.gz} and {A22153A1_2.clean.fq.gz}, calculate the expression profile with kallisto. | 1 | [
"Generate expression results"
] | 1 | [
"Calculate expression levels and generate expression files."
] | [
{
"name": "expression.txt",
"path": "downloaded_files\\index_0069\\milestone\\expression.txt"
}
] | RNA | Rep2 | clear | round3-IMF | [
{
"name": "A22153A1_1.clean.fq.gz",
"path": "downloaded_files\\index_0069\\files\\A22153A1_1.clean.fq.gz"
},
{
"name": "A22153A1_2.clean.fq.gz",
"path": "downloaded_files\\index_0069\\files\\A22153A1_2.clean.fq.gz"
}
] | 12.33 | RNA | 69 |
O | Kallisto | Calculate the expression profile for RNA-seq sequencing data {A22153A1_1.clean.fq.gz} and {A22153A1_2.clean.fq.gz} for humans. | 1 | [
"Generate expression results"
] | 1 | [
"Calculate expression levels and generate expression files."
] | [
{
"name": "expression.txt",
"path": "downloaded_files\\index_0070\\milestone\\expression.txt"
}
] | RNA | Rep2 | open | round3-IMF | [
{
"name": "A22153A1_1.clean.fq.gz",
"path": "downloaded_files\\index_0070\\files\\A22153A1_1.clean.fq.gz"
},
{
"name": "A22153A1_2.clean.fq.gz",
"path": "downloaded_files\\index_0070\\files\\A22153A1_2.clean.fq.gz"
}
] | 12.34 | RNA | 70 |
O | Kallisto | {For RNA-seq sequencing data of individuals {A22158A1_1.clean.fq.gz} and {A22158A1_2.clean.fq.gz}, use kallisto to calculate the expression profile} | 1 | [
"Generate expression results"
] | 1 | [
"Calculate expression levels and generate expression files."
] | [
{
"name": "expression.txt",
"path": "downloaded_files\\index_0071\\milestone\\expression.txt"
}
] | RNA | Rep3 | clear | round3-IMF | [
{
"name": "A22158A1_1.clean.fq.gz",
"path": "downloaded_files\\index_0071\\files\\A22158A1_1.clean.fq.gz"
},
{
"name": "A22158A1_2.clean.fq.gz",
"path": "downloaded_files\\index_0071\\files\\A22158A1_2.clean.fq.gz"
}
] | 12.35 | RNA | 71 |
O | Kallisto | Calculate the expression profile for the RNA-seq sequencing data of the individual {A22158A1_1.clean.fq.gz} and {A22158A1_2.clean.fq.gz}. | 1 | [
"Generate expression results"
] | 1 | [
"Calculate expression levels and generate expression files"
] | [
{
"name": "expression.txt",
"path": "downloaded_files\\index_0072\\milestone\\expression.txt"
}
] | RNA | Rep3 | open | round3-IMF | [
{
"name": "A22158A1_1.clean.fq.gz",
"path": "downloaded_files\\index_0072\\files\\A22158A1_1.clean.fq.gz"
},
{
"name": "A22158A1_2.clean.fq.gz",
"path": "downloaded_files\\index_0072\\files\\A22158A1_2.clean.fq.gz"
}
] | 12.36 | RNA | 72 |
O | MicroArray | {The following is the chip expression data of one sample from the human embryonic stem cell dataset GSE16919 {GSM424314.CEL.gz}, with the corresponding probe number {GPL571}. Please use the CeltoExp tool to convert the CEL chip expression file into a gene expression profile data in txt file format.} | 1 | [
"Generate the file matrix.txt"
] | 1 | [
"Using the celtoexp tool to convert chip data into gene expression profiles."
] | [
{
"name": "matrix.txt",
"path": "downloaded_files\\index_0073\\milestone\\matrix.txt"
}
] | RNA | Rep1 | clear | round3-IMF | [
{
"name": "GSM424314.CEL.gz",
"path": "downloaded_files\\index_0073\\files\\GSM424314.CEL.gz"
}
] | 12.37 | RNA | 73 |
O | MicroArray | {The following is the chip expression data of one sample from the human embryonic stem cell dataset GSE16919 {GSM424314.CEL.gz}, with the corresponding probe number being {GPL571}. Please convert the CEL file into a gene expression profile.} | 1 | [
"Generate the file matrix.txt"
] | 1 | [
"Using the celtoexp tool to convert chip data into gene expression profiles."
] | [
{
"name": "matrix.txt",
"path": "downloaded_files\\index_0074\\milestone\\matrix.txt"
}
] | RNA | Rep1 | open | round3-IMF | [
{
"name": "GSM424314.CEL.gz",
"path": "downloaded_files\\index_0074\\files\\GSM424314.CEL.gz"
}
] | 12.38 | RNA | 74 |
O | MicroArray | {The following is the chip expression data of one sample from the esophageal squamous cell carcinoma dataset GSE20347 {GSM509787_E1507N.CEL.gz}, corresponding to the probe number {GPL571}. Please use the CeltoExp tool to convert the CEL chip expression file into a txt file format of gene expression profile data.} | 1 | [
"Generate the file matrix.txt"
] | 1 | [
"Using the celtoexp tool to convert chip data into gene expression profiles."
] | [
{
"name": "matrix.txt",
"path": "downloaded_files\\index_0075\\milestone\\matrix.txt"
}
] | RNA | Rep2 | clear | round3-IMF | [
{
"name": "GSM509787_E1507N.CEL.gz",
"path": "downloaded_files\\index_0075\\files\\GSM509787_E1507N.CEL.gz"
}
] | 12.39 | RNA | 75 |
O | MicroArray | {The following is the chip expression data of one sample from the esophageal squamous cell carcinoma dataset GSE20347 {GSM509787_E1507N.CEL.gz}, corresponding to the probe number {GPL571}. Please convert the CEL file into a gene expression profile.} | 1 | [
"Generate the file matrix.txt"
] | 1 | [
"Using the celtoexp tool to convert chip data into gene expression profiles."
] | [
{
"name": "matrix.txt",
"path": "downloaded_files\\index_0076\\milestone\\matrix.txt"
}
] | RNA | Rep2 | open | round3-IMF | [
{
"name": "GSM509787_E1507N.CEL.gz",
"path": "downloaded_files\\index_0076\\files\\GSM509787_E1507N.CEL.gz"
}
] | 12.4 | RNA | 76 |
O | MicroArray | {The following is the chip expression data of one sample from the breast cancer dataset GSE24578 {GSM605912.CEL.gz}, with the corresponding probe number being {GPL571}. Please use the CeltoExp tool to convert the CEL chip expression file into a gene expression profile data in txt file format.} | 1 | [
"Generate the file matrix.txt"
] | 1 | [
"Using the celtoexp tool to convert chip data into gene expression profiles."
] | [
{
"name": "matrix.txt",
"path": "downloaded_files\\index_0077\\milestone\\matrix.txt"
}
] | RNA | Rep3 | clear | round3-IMF | [
{
"name": "GSM605912.CEL.gz",
"path": "downloaded_files\\index_0077\\files\\GSM605912.CEL.gz"
}
] | 12.41 | RNA | 77 |
O | MicroArray | {The following is the chip expression data of one sample from the breast cancer dataset GSE24578 {GSM605912.CEL.gz}, corresponding to the probe number {GPL571}. Please convert the CEL file into a gene expression profile.} | 1 | [
"Generate the file matrix.txt"
] | 1 | [
"Using the celtoexp tool to convert chip data into gene expression profiles."
] | [
{
"name": "matrix.txt",
"path": "downloaded_files\\index_0078\\milestone\\matrix.txt"
}
] | RNA | Rep3 | open | round3-IMF | [
{
"name": "GSM605912.CEL.gz",
"path": "downloaded_files\\index_0078\\files\\GSM605912.CEL.gz"
}
] | 12.42 | RNA | 78 |
O | Seruat marker genes | {{genes.tsv},{matrix.mtx},{barcodes.tsv} The three files are the 10X Genomics single-cell transcriptome dataset of peripheral blood mononuclear cells (PBMC). This dataset contains 2,700 single cells. Please preprocess this dataset, filter and select the cells, normalize the data, then use Seurat to cluster the cells, a... | 3 | [
"Violin plot QC_metrics.png of the standardized Seurat object",
"Top 10 Differentially Expressed Genes Clusters_Marker.csv",
"Generate UMAP graph clusters_umap.png"
] | 3 | [
"Read single-cell transcriptome data, use Seurat for data preprocessing, filter out low-quality cells and low-expressed genes, and perform normalization to obtain the preprocessed Seurat object.",
"Cell clustering was performed on the preprocessed Seurat object to obtain the top 10 differential genes for each clu... | [
{
"name": "QC_metrics.png",
"path": "downloaded_files\\index_0079\\milestone\\QC_metrics.png"
},
{
"name": "clusters_Marker.csv",
"path": "downloaded_files\\index_0079\\milestone\\clusters_Marker.csv"
},
{
"name": "clusters_umap.png",
"path": "downloaded_files\\index_0079\\milestone\... | scRNA | Rep1 | clear | round3-IMF | [
{
"name": "genes.tsv",
"path": "downloaded_files\\index_0079\\files\\genes.tsv"
},
{
"name": "matrix.mtx",
"path": "downloaded_files\\index_0079\\files\\matrix.mtx"
},
{
"name": "barcodes.tsv",
"path": "downloaded_files\\index_0079\\files\\barcodes.tsv"
}
] | 13.01 | scRNA | 79 |
O | Seruat marker genes | The three files {{genes.tsv},{matrix.mtx},{barcodes.tsv}} are the 10X Genomics single-cell transcriptome dataset of peripheral blood mononuclear cells (PBMC). Obtain the top 10 differential genes for each cluster after cell clustering in the 10X single-cell transcriptome dataset. | 2 | [
"Violin plot QC_metrics.png of the standardized Seurat object",
"Top 10 Differentially Expressed Genes Clusters_Marker.csv"
] | 2 | [
"Read single-cell transcriptome data, use Seurat for data preprocessing, filter out low-quality cells and low-expressed genes, and perform normalization to obtain the preprocessed Seurat object.",
"Cell clustering was performed on the preprocessed Seurat object to obtain the top 10 differential genes for each clu... | [
{
"name": "QC_metrics.png",
"path": "downloaded_files\\index_0080\\milestone\\QC_metrics.png"
},
{
"name": "clusters_Marker.csv",
"path": "downloaded_files\\index_0080\\milestone\\clusters_Marker.csv"
}
] | scRNA | Rep1 | open | round3-IMF | [
{
"name": "genes.tsv",
"path": "downloaded_files\\index_0080\\files\\genes.tsv"
},
{
"name": "matrix.mtx",
"path": "downloaded_files\\index_0080\\files\\matrix.mtx"
},
{
"name": "barcodes.tsv",
"path": "downloaded_files\\index_0080\\files\\barcodes.tsv"
}
] | 13.02 | scRNA | 80 |
O | Seruat marker genes | The three files { {genes.tsv},{matrix.mtx},{barcodes.tsv} } are the 10X Genomics single-cell transcriptome dataset of peripheral blood mononuclear cells (PBMC). This dataset contains 2,700 single cells. Please preprocess the dataset, filter and select the cells, normalize the data, then use Seurat to cluster the cells,... | 4 | [
"Violin plot QC_metrics.png of the standardized Seurat object",
"Top 10 Differentially Expressed Genes Clusters_Marker.csv",
"Generate cell clustering UMAP graph clusters_umap.png",
"Generate cell annotation results annotation_umap.png"
] | 4 | [
"Read single-cell transcriptome data, use Seurat for data preprocessing, filter out low-quality cells and low-expressed genes, and perform normalization to obtain the preprocessed Seurat object.",
"Cell clustering was performed on the preprocessed Seurat object to obtain the top 10 differential genes for each clu... | [
{
"name": "QC_metrics.png",
"path": "downloaded_files\\index_0081\\milestone\\QC_metrics.png"
},
{
"name": "clusters_Marker.csv",
"path": "downloaded_files\\index_0081\\milestone\\clusters_Marker.csv"
},
{
"name": "clusters_umap.png",
"path": "downloaded_files\\index_0081\\milestone\... | scRNA | Rep2 | clear | round3-IMF | [
{
"name": "genes.tsv",
"path": "downloaded_files\\index_0081\\files\\genes.tsv"
},
{
"name": "matrix.mtx",
"path": "downloaded_files\\index_0081\\files\\matrix.mtx"
},
{
"name": "barcodes.tsv",
"path": "downloaded_files\\index_0081\\files\\barcodes.tsv"
}
] | 13.03 | scRNA | 81 |
O | Seruat marker genes | The three files { {genes.tsv},{matrix.mtx},{barcodes.tsv} } are the 10X Genomics single-cell transcriptome dataset of peripheral blood mononuclear cells (PBMC). Perform cell annotation and visualization on the 10X single-cell transcriptome dataset. | 3 | [
"Violin plot QC_metrics.png of the standardized Seurat object",
"Top 10 Differentially Expressed Genes Clusters_Marker.csv",
"Generate cell annotation results annotation_umap.png"
] | 3 | [
"Read single-cell transcriptome data, use Seurat for data preprocessing, filter out low-quality cells and low-expressed genes, and perform normalization to obtain the preprocessed Seurat object.",
"Cell clustering was performed on the preprocessed Seurat object to obtain the top 10 differential genes for each clu... | [
{
"name": "QC_metrics.png",
"path": "downloaded_files\\index_0082\\milestone\\QC_metrics.png"
},
{
"name": "clusters_Marker.csv",
"path": "downloaded_files\\index_0082\\milestone\\clusters_Marker.csv"
},
{
"name": "annotion_umap.png",
"path": "downloaded_files\\index_0082\\milestone\... | scRNA | Rep2 | open | round3-IMF | [
{
"name": "genes.tsv",
"path": "downloaded_files\\index_0082\\files\\genes.tsv"
},
{
"name": "matrix.mtx",
"path": "downloaded_files\\index_0082\\files\\matrix.mtx"
},
{
"name": "barcodes.tsv",
"path": "downloaded_files\\index_0082\\files\\barcodes.tsv"
}
] | 13.04 | scRNA | 82 |
O | Seruat marker genes | The {{Epithelial_ESCC.RDS} file is single-cell transcriptome data of malignant epithelial cells from esophageal cancer obtained using 10X Genomics single-cell RNA sequencing technology. The file was created using R language and the Seurat package. This dataset includes patients who have developed resistance to various ... | 2 | [
"Top 10 Differentially Expressed Genes Clusters_Marker.csv",
"Generate cell clustering UMAP graph clusters_umap.png"
] | 2 | [
"Cell clustering was performed on the preprocessed Seurat object to obtain the top 10 differential genes for each cluster.",
"Use UMAP nonlinear dimensionality reduction to visualize clustering data."
] | [
{
"name": "clusters_Marker.csv",
"path": "downloaded_files\\index_0083\\milestone\\clusters_Marker.csv"
},
{
"name": "clusters_umap.png",
"path": "downloaded_files\\index_0083\\milestone\\clusters_umap.png"
}
] | scRNA | Rep3 | clear | round3-IMF | [
{
"name": "Epithelial_ESCC.RDS",
"path": "downloaded_files\\index_0083\\files\\Epithelial_ESCC.RDS"
}
] | 13.05 | scRNA | 83 |
O | Seruat marker genes | The {{Epithelial_ESCC.RDS} file is single-cell transcriptome data of malignant epithelial cells from esophageal cancer obtained using 10X Genomics single-cell RNA sequencing technology, which retrieves the top 10 differential genes for each cluster after cell clustering in the 10X single-cell transcriptome dataset. | 2 | [
"Top 10 Differentially Expressed Genes Clusters_Marker.csv",
"Generate cell clustering UMAP graph clusters_umap.png"
] | 2 | [
"Cell clustering was performed on the preprocessed Seurat object to obtain the top 10 differential genes for each cluster.",
"Use UMAP nonlinear dimensionality reduction to visualize clustering data."
] | [
{
"name": "clusters_Marker.csv",
"path": "downloaded_files\\index_0084\\milestone\\clusters_Marker.csv"
},
{
"name": "clusters_umap.png",
"path": "downloaded_files\\index_0084\\milestone\\clusters_umap.png"
}
] | scRNA | Rep3 | open | round3-IMF | [
{
"name": "Epithelial_ESCC.RDS",
"path": "downloaded_files\\index_0084\\files\\Epithelial_ESCC.RDS"
}
] | 13.06 | scRNA | 84 |
O | Seruat clustering | The {{Epithelial_ESCC.RDS} file is single-cell transcriptome data of malignant epithelial cells from esophageal cancer obtained through 10X Genomics single-cell RNA sequencing technology. The file was created using R language and the Seurat package. This dataset includes patients who have developed resistance to variou... | 3 | [
"Top 10 Differentially Expressed Genes Clusters_Marker.csv",
"Generate cell clustering UMAP graph clusters_umap.png",
"Generate the GO enrichment results for each cluster GO.tsv"
] | 3 | [
"Cell clustering was performed on the preprocessed Seurat object to obtain the top 10 differential genes for each cluster.",
"Use UMAP nonlinear dimensionality reduction to visualize clustering data.",
"Use clusterProfiler for GO/KEGG functional enrichment of each cluster."
] | [
{
"name": "clusters_Marker.csv",
"path": "downloaded_files\\index_0085\\milestone\\clusters_Marker.csv"
},
{
"name": "clusters_umap.png",
"path": "downloaded_files\\index_0085\\milestone\\clusters_umap.png"
},
{
"name": "GO.tsv",
"path": "downloaded_files\\index_0085\\milestone\\GO.t... | scRNA | Rep1 | clear | round3-IMF | [
{
"name": "Epithelial_ESCC.RDS",
"path": "downloaded_files\\index_0085\\files\\Epithelial_ESCC.RDS"
}
] | 13.07 | scRNA | 85 |
O | Seruat clustering | The {{Epithelial_ESCC.RDS} file is single-cell transcriptome data of malignant epithelial cells from esophageal cancer obtained using 10X Genomics single-cell RNA sequencing technology, and functional enrichment is performed based on the cell clustering results of the 10X single-cell transcriptome dataset. | 3 | [
"Top 10 Differentially Expressed Genes Clusters_Marker.csv",
"Generate cell clustering UMAP graph clusters_umap.png",
"Generate the GO enrichment results for each cluster GO.tsv"
] | 3 | [
"Cell clustering was performed on the preprocessed Seurat object to obtain the top 10 differential genes for each cluster.",
"Use UMAP nonlinear dimensionality reduction to visualize clustering data.",
"Use clusterProfiler for GO/KEGG functional enrichment of each cluster."
] | [
{
"name": "clusters_Marker.csv",
"path": "downloaded_files\\index_0086\\milestone\\clusters_Marker.csv"
},
{
"name": "clusters_umap.png",
"path": "downloaded_files\\index_0086\\milestone\\clusters_umap.png"
},
{
"name": "GO.tsv",
"path": "downloaded_files\\index_0086\\milestone\\GO.t... | scRNA | Rep1 | open | round3-IMF | [
{
"name": "Epithelial_ESCC.RDS",
"path": "downloaded_files\\index_0086\\files\\Epithelial_ESCC.RDS"
}
] | 13.08 | scRNA | 86 |
O | Seruat clustering | The three files { {genes.tsv},{matrix.mtx},{barcodes.tsv} } are the 10X Genomics single-cell transcriptome dataset of peripheral blood mononuclear cells (PBMC). This dataset contains approximately 68,000 cells, sequenced under Illumina NextSeq 500, with about 20,000 reads per cell. Please preprocess this dataset, filte... | 3 | [
"Violin plot QC_metrics.png of the standardized Seurat object",
"Top 10 Differentially Expressed Genes Clusters_Marker.csv",
"Generate clusters_umap.png"
] | 3 | [
"Read single-cell transcriptome data, use Seurat for data preprocessing, filter out low-quality cells and low-expressed genes, and perform normalization to obtain the preprocessed Seurat object.",
"Cell clustering was performed on the preprocessed Seurat object to obtain the top 10 differential genes for each clu... | [
{
"name": "QC_metrics.png",
"path": "downloaded_files\\index_0087\\milestone\\QC_metrics.png"
},
{
"name": "clusters_Marker.csv",
"path": "downloaded_files\\index_0087\\milestone\\clusters_Marker.csv"
},
{
"name": "clusters_umap.png",
"path": "downloaded_files\\index_0087\\milestone\... | scRNA | Rep2 | clear | round3-IMF | [
{
"name": "genes.tsv",
"path": "downloaded_files\\index_0087\\files\\genes.tsv"
},
{
"name": "matrix.mtx",
"path": "downloaded_files\\index_0087\\files\\matrix.mtx"
},
{
"name": "barcodes.tsv",
"path": "downloaded_files\\index_0087\\files\\barcodes.tsv"
}
] | 13.09 | scRNA | 87 |
O | Seruat clustering | The three files {{genes.tsv},{matrix.mtx},{barcodes.tsv} are the 10X Genomics single-cell transcriptome dataset of peripheral blood mononuclear cells (PBMC). Obtain the top 10 differential genes for each cluster after cell clustering in the 10X single-cell transcriptome dataset. | 2 | [
"Violin plot QC_metrics.png of the standardized Seurat object",
"Top 10 Differentially Expressed Genes Clusters_Marker.csv"
] | 2 | [
"Read single-cell transcriptome data, use Seurat for data preprocessing, filter out low-quality cells and low-expressed genes, and perform normalization to obtain the preprocessed Seurat object.",
"Cell clustering was performed on the preprocessed Seurat object to obtain the top 10 differential genes for each clu... | [
{
"name": "QC_metrics.png",
"path": "downloaded_files\\index_0088\\milestone\\QC_metrics.png"
},
{
"name": "clusters_Marker.csv",
"path": "downloaded_files\\index_0088\\milestone\\clusters_Marker.csv"
}
] | scRNA | Rep2 | open | round3-IMF | [
{
"name": "genes.tsv",
"path": "downloaded_files\\index_0088\\files\\genes.tsv"
},
{
"name": "matrix.mtx",
"path": "downloaded_files\\index_0088\\files\\matrix.mtx"
},
{
"name": "barcodes.tsv",
"path": "downloaded_files\\index_0088\\files\\barcodes.tsv"
}
] | 13.1 | scRNA | 88 |
O | Seruat clustering | {{genes.tsv},{matrix.mtx},{barcodes.tsv} three files are the 10X Genomics single-cell transcriptome dataset of peripheral blood mononuclear cells (PBMC). This dataset contains approximately 68,000 cells, sequenced under Illumina NextSeq 500, with about 20,000 reads per cell. Please preprocess this dataset, filter and s... | 4 | [
"Violin plot QC_metrics.png of the standardized Seurat object",
"Top 10 Differentially Expressed Genes Clusters_Marker.csv",
"Generate cell clustering UMAP graph clusters_umap.png",
"Generate cell annotation results annotation_umap.png"
] | 4 | [
"Read single-cell transcriptome data, use Seurat for data preprocessing, filter out low-quality cells and low-expressed genes, and perform normalization to obtain the preprocessed Seurat object.",
"Cell clustering was performed on the preprocessed Seurat object to obtain the top 10 differential genes for each clu... | [
{
"name": "QC_metrics.png",
"path": "downloaded_files\\index_0089\\milestone\\QC_metrics.png"
},
{
"name": "clusters_Marker.csv",
"path": "downloaded_files\\index_0089\\milestone\\clusters_Marker.csv"
},
{
"name": "clusters_umap.png",
"path": "downloaded_files\\index_0089\\milestone\... | scRNA | Rep3 | clear | round3-IMF | [
{
"name": "genes.tsv",
"path": "downloaded_files\\index_0089\\files\\genes.tsv"
},
{
"name": "matrix.mtx",
"path": "downloaded_files\\index_0089\\files\\matrix.mtx"
},
{
"name": "barcodes.tsv",
"path": "downloaded_files\\index_0089\\files\\barcodes.tsv"
}
] | 13.11 | scRNA | 89 |
O | Seruat clustering | The three files {{genes.tsv}, {matrix.mtx}, {barcodes.tsv}} are the 10X Genomics single-cell transcriptome dataset of peripheral blood mononuclear cells (PBMC). Perform cell annotation and visualization on the 10X single-cell transcriptome dataset. | 3 | [
"Violin plot QC_metrics.png of the standardized Seurat object",
"Top 10 Differentially Expressed Genes Clusters_Marker.csv",
"Generate cell annotation results annotation_umap.png"
] | 3 | [
"Read single-cell transcriptome data, use Seurat for data preprocessing, filter out low-quality cells and low-expressed genes, and perform normalization to obtain the preprocessed Seurat object.",
"Cell clustering was performed on the preprocessed Seurat object to obtain the top 10 differential genes for each clu... | [
{
"name": "QC_metrics.png",
"path": "downloaded_files\\index_0090\\milestone\\QC_metrics.png"
},
{
"name": "clusters_Marker.csv",
"path": "downloaded_files\\index_0090\\milestone\\clusters_Marker.csv"
},
{
"name": "annotion_umap.png",
"path": "downloaded_files\\index_0090\\milestone\... | scRNA | Rep3 | open | round3-IMF | [
{
"name": "genes.tsv",
"path": "downloaded_files\\index_0090\\files\\genes.tsv"
},
{
"name": "matrix.mtx",
"path": "downloaded_files\\index_0090\\files\\matrix.mtx"
},
{
"name": "barcodes.tsv",
"path": "downloaded_files\\index_0090\\files\\barcodes.tsv"
}
] | 13.12 | scRNA | 90 |
O | Cellranger count | {The following is the storage path of the 10X high-throughput sequencing data from peripheral blood cell data {pbmc_1k_v3_fastqs.tar.gz}, consisting of lymphocytes (T cells, B cells, and NK cells) and monocytes. Please use the cellranger_count tool to align the sequencing reads in the FASTQ files with the reference tra... | 1 | [
"Generated downstream analysis files such as filtered_feature_bc_matrix.h5."
] | 1 | [
"Use Cell Ranger to align the FASTQ files with the reference transcriptome and generate downstream analysis files."
] | [
{
"name": "filtered_feature_bc_matrix.h5",
"path": "downloaded_files\\index_0091\\milestone\\filtered_feature_bc_matrix.h5"
}
] | scRNA | Rep1 | clear | round3-IMF | [
{
"name": "pbmc_1k_v3_fastqs.tar.gz",
"path": "downloaded_files\\index_0091\\files\\pbmc_1k_v3_fastqs.tar.gz"
}
] | 13.13 | scRNA | 91 |
O | Cellranger count | {The following is the storage path of the 10X high-throughput sequencing data from human peripheral blood cell data {pbmc_1k_v3_fastqs.tar.gz}, consisting of lymphocytes (T cells, B cells, and NK cells) and monocytes. Please generate downstream analysis files.} | 1 | [
"Generated downstream files such as filtered_feature_bc_matrix.h5."
] | 1 | [
"Use Cell Ranger to align the FASTQ files with the reference transcriptome and generate downstream analysis files."
] | [
{
"name": "filtered_feature_bc_matrix.h5",
"path": "downloaded_files\\index_0092\\milestone\\filtered_feature_bc_matrix.h5"
}
] | scRNA | Rep1 | open | round3-IMF | [
{
"name": "pbmc_1k_v3_fastqs.tar.gz",
"path": "downloaded_files\\index_0092\\files\\pbmc_1k_v3_fastqs.tar.gz"
}
] | 13.14 | scRNA | 92 |
O | Cellranger count | {The following is the storage path of the 10X high-throughput sequencing data of infiltrative ductal carcinoma cells from human donors {Breast_Cancer_3p_LT_fastqs.tar.gz}. Please use the cellranger_count tool to align the sequencing reads in the FASTQ files with the reference transcriptome and generate downstream analy... | 1 | [
"Generated downstream analysis files such as filtered_feature_bc_matrix.h5."
] | 1 | [
"Use Cell Ranger to align the FASTQ files with the reference transcriptome and generate downstream analysis files."
] | [
{
"name": "filtered_feature_bc_matrix.h5",
"path": "downloaded_files\\index_0093\\milestone\\filtered_feature_bc_matrix.h5"
}
] | scRNA | Rep2 | clear | round3-IMF | [
{
"name": "Breast_Cancer_3p_LT_fastqs.tar.gz",
"path": "downloaded_files\\index_0093\\files\\Breast_Cancer_3p_LT_fastqs.tar.gz"
}
] | 13.15 | scRNA | 93 |
O | Cellranger count | {The following is the storage path of the 10X high-throughput sequencing data of infiltrating ductal carcinoma cells from human donors {Breast_Cancer_3p_LT_fastqs.tar.gz}, please generate downstream analysis files.} | 1 | [
"Generated downstream analysis files such as filtered_feature_bc_matrix.h5."
] | 1 | [
"Use Cell Ranger to align the FASTQ files with the reference transcriptome and generate downstream analysis files."
] | [
{
"name": "filtered_feature_bc_matrix.h5",
"path": "downloaded_files\\index_0094\\milestone\\filtered_feature_bc_matrix.h5"
}
] | scRNA | Rep2 | open | round3-IMF | [
{
"name": "Breast_Cancer_3p_LT_fastqs.tar.gz",
"path": "downloaded_files\\index_0094\\files\\Breast_Cancer_3p_LT_fastqs.tar.gz"
}
] | 13.16 | scRNA | 94 |
O | Cellranger count | {The following is the storage path of the 10X high-throughput sequencing data from the data of polymorphic glioblastoma cells {Brain_Tumor_3p_LT_fastqs.tar.gz}. Please use the cellranger_count tool to align the sequencing reads in the FASTQ files with the reference transcriptome and generate downstream analysis files.} | 1 | [
"Generated downstream analysis files such as filtered_feature_bc_matrix.h5."
] | 1 | [
"Use Cell Ranger to align the FASTQ files with the reference transcriptome and generate downstream analysis files."
] | [
{
"name": "filtered_feature_bc_matrix.h5",
"path": "downloaded_files\\index_0095\\milestone\\filtered_feature_bc_matrix.h5"
}
] | scRNA | Rep3 | clear | round3-IMF | [
{
"name": "Brain_Tumor_3p_LT_fastqs.tar.gz",
"path": "downloaded_files\\index_0095\\files\\Brain_Tumor_3p_LT_fastqs.tar.gz"
}
] | 13.17 | scRNA | 95 |
O | Cellranger count | {The following is the storage path for the 10X high-throughput sequencing data from the polymorphic glioblastoma cell data {Brain_Tumor_3p_LT_fastqs.tar.gz}, please generate downstream analysis files.} | 1 | [
"Generated downstream analysis files such as filtered_feature_bc_matrix.h5."
] | 1 | [
"Use Cell Ranger to align the FASTQ files with the reference transcriptome and generate downstream analysis files."
] | [
{
"name": "filtered_feature_bc_matrix.h5",
"path": "downloaded_files\\index_0096\\milestone\\filtered_feature_bc_matrix.h5"
}
] | scRNA | Rep3 | open | round3-IMF | [
{
"name": "Brain_Tumor_3p_LT_fastqs.tar.gz",
"path": "downloaded_files\\index_0096\\files\\Brain_Tumor_3p_LT_fastqs.tar.gz"
}
] | 13.18 | scRNA | 96 |
P | Rare disease | {You are analyzing the genetic testing data of a patient with a genetic disease. The patient's variant data is stored in the following files: {MUT_edit1.fastq.gz}{MUT_edit2.fastq.gz}. Please use the GATK best practice workflow for variant identification. Based on the variant results, please identify the pathogenic vari... | 4 | [
"Generate comparison result: bam",
"Generate VCF",
"There are records of mutation results: chr6-49427089-A-G, chr6-49419304-A-G.",
"Associated diseases recorded: Methylmalonic aciduria, mut(0) type."
] | 4 | [
"Compare and generate bam (additional steps can include: sort, dedup, gatk-bqsr).",
"Identification and generation of VCF",
"Comment, generate maf",
"Associated Diseases"
] | [
{
"name": "tumor.recal.bam",
"path": "downloaded_files\\index_0097\\milestone\\tumor.recal.bam"
},
{
"name": "tumor.recal.vcf",
"path": "downloaded_files\\index_0097\\milestone\\tumor.recal.vcf"
},
{
"name": "tumor.recal.fil.maf",
"path": "downloaded_files\\index_0097\\milestone\\tum... | Genetic | Rep1 | clear | round3-IMF | [
{
"name": "MUT_edit1.fastq.gz",
"path": "downloaded_files\\index_0097\\files\\MUT_edit1.fastq.gz"
},
{
"name": "MUT_edit2.fastq.gz",
"path": "downloaded_files\\index_0097\\files\\MUT_edit2.fastq.gz"
}
] | 21.01 | Genetic | 97 |
P | Rare disease | You are analyzing the genetic testing data of a patient with a genetic disease. The patient's mutation data is stored in the following files: {MUT_edit1.fastq.gz}{MUT_edit2.fastq.gz}. You need to first identify the mutations, and based on the results of the mutations, please identify the patient's pathogenic mutations ... | 4 | [
"Generate comparison result: bam",
"Generate VCF",
"There are records of mutation results: chr6-49427089-A-G, chr6-49419304-A-G.",
"Associated diseases recorded: Methylmalonic aciduria, mut(0) type."
] | 4 | [
"Compare and generate bam (additional steps can include: sort, dedup, gatk-bqsr).",
"Identification and generation of VCF",
"Comment, generate maf",
"Associated Diseases"
] | [
{
"name": "tumor.recal.bam",
"path": "downloaded_files\\index_0098\\milestone\\tumor.recal.bam"
},
{
"name": "tumor.recal.vcf",
"path": "downloaded_files\\index_0098\\milestone\\tumor.recal.vcf"
},
{
"name": "tumor.recal.fil.maf",
"path": "downloaded_files\\index_0098\\milestone\\tum... | Genetic | Rep1 | open | round3-IMF | [
{
"name": "MUT_edit1.fastq.gz",
"path": "downloaded_files\\index_0098\\files\\MUT_edit1.fastq.gz"
},
{
"name": "MUT_edit2.fastq.gz",
"path": "downloaded_files\\index_0098\\files\\MUT_edit2.fastq.gz"
}
] | 21.02 | Genetic | 98 |
P | Rare disease | You are analyzing the genetic testing data of a patient with a genetic disease. The patient's variant data is stored in the following files: {PAH_edit1.fastq.gz}{PAH_edit2.fastq.gz}. Please use the GATK best practice workflow for variant identification. Based on the variant results, please identify the pathogenic varia... | 4 | [
"Generate comparison result: bam",
"Generate VCF",
"There are records of mutation results: chr12-103246594-G-A, chr12-103246707-C-T.",
"Associated diseases recorded: Phenylketonuria; Hyperphenylalaninemia, non-PKU mild."
] | 4 | [
"Compare and generate bam (additional steps can include: sort, dedup, gatk-bqsr).",
"Identification and generation of VCF",
"Comment, generate maf",
"Associated Diseases"
] | [
{
"name": "tumor.recal.bam",
"path": "downloaded_files\\index_0099\\milestone\\tumor.recal.bam"
},
{
"name": "tumor.recal.vcf",
"path": "downloaded_files\\index_0099\\milestone\\tumor.recal.vcf"
},
{
"name": "tumor.recal.fil.maf",
"path": "downloaded_files\\index_0099\\milestone\\tum... | Genetic | Rep2 | clear | round3-IMF | [
{
"name": "PAH_edit1.fastq.gz",
"path": "downloaded_files\\index_0099\\files\\PAH_edit1.fastq.gz"
},
{
"name": "PAH_edit2.fastq.gz",
"path": "downloaded_files\\index_0099\\files\\PAH_edit2.fastq.gz"
}
] | 21.03 | Genetic | 99 |
P | Rare disease | You are analyzing the genetic testing data of a patient with a genetic disease. The patient's variant data is stored in the following files: {PAH_edit1.fastq.gz}{PAH_edit2.fastq.gz}. You need to first identify the variants, and based on the results of the variants, please identify the patient's pathogenic variants and ... | 4 | [
"Generate comparison result: bam",
"Generate VCF",
"There are records of mutation results: chr12-103246594-G-A, chr12-103246707-C-T.",
"Associated diseases recorded: Phenylketonuria; Hyperphenylalaninemia, non-PKU mild."
] | 4 | [
"Compare and generate bam (additional steps can include: sort, dedup, gatk-bqsr).",
"Identification and generation of VCF",
"Comment, generate maf",
"Associated Diseases"
] | [
{
"name": "tumor.recal.bam",
"path": "downloaded_files\\index_0100\\milestone\\tumor.recal.bam"
},
{
"name": "tumor.recal.vcf",
"path": "downloaded_files\\index_0100\\milestone\\tumor.recal.vcf"
},
{
"name": "tumor.recal.fil.maf",
"path": "downloaded_files\\index_0100\\milestone\\tum... | Genetic | Rep2 | open | round3-IMF | [
{
"name": "PAH_edit1.fastq.gz",
"path": "downloaded_files\\index_0100\\files\\PAH_edit1.fastq.gz"
},
{
"name": "PAH_edit2.fastq.gz",
"path": "downloaded_files\\index_0100\\files\\PAH_edit2.fastq.gz"
}
] | 21.04 | Genetic | 100 |
End of preview. Expand in Data Studio
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Check out the documentation for more information.
BioMed AQA Dataset
Dataset Description
This dataset contains 327 biomedical analysis questions and tasks, covering various bioinformatics workflows including genomics, transcriptomics, and other molecular biology data analysis tasks.
Dataset Structure
Data Fields
Each record in the dataset contains the following fields:
task_classification(string): Classification of the task typetask_abbr(string): Abbreviated name of the taskquestion(string): The main question or task descriptionreference_milestone_count(integer): Number of reference milestonesreference_milestones(list of strings): List of milestone descriptionsreference_step_count(integer): Number of reference stepsreference_steps(list of strings): List of step descriptionsmilestone_reference_files(list of objects): Reference files for milestones, each containing:name(string): File namepath(string): File path
task_subclass(string): Subclass of the task (e.g., DNA, RNA, Protein)dataset(string): Dataset identifierquestion_type(string): Type of question (e.g., clear, vague)test(string): Test identifierfiles(list of objects): Input files required for the task, each containing:name(string): File namepath(string): File path
order(float): Order identifiertask(string): Task categoryindex(integer): Record index
Dataset Statistics
- Total records: 327
- Total files: 1,028 files (~30 GB)
Task Classification Distribution
- O (Omics Analysis): 96 records
- V (Visualization): 96 records
- M (Machine Learning): 59 records
- S (Statistics): 58 records
- P (Protein Analysis): 18 records
Task Subclass Distribution
- RNA: 42 records
- Inferential statistics: 38 records
- DNA: 36 records
- Basic plot: 26 records
- Fitting & inference: 23 records
- Descriptive statistics: 20 records
- scRNA: 18 records
- Upset: 18 records
- Classification: 16 records
- Regression: 14 records
- Clustering: 13 records
- Tumor: 12 records
- Surivival: 12 records
- Feature selection: 10 records
- Genetic: 6 records
- Deep Learning: 6 records
- Other: 17 records
Question Type Distribution
- Clear questions: 202 records
- Open questions: 125 records
Data Files
The dataset references a collection of files located in the downloaded_files directory. These files include:
- FASTQ files (.fastq.gz): 283 files - Raw sequencing data
- TSV files (.tsv): 276 files - Tabular data
- GZ compressed files (.gz): 132 files - Compressed data files
- BAM files (.bam): Aligned sequence data
- VCF files (.vcf): Variant calling format files
- MAF files (.maf): Mutation annotation format files
- CSV files: Comma-separated value files
- And other bioinformatics file formats
Total: 1,028 files, approximately 31.1 GB
Download Data Files
The data files are archived and available for download from Zenodo:
The dataset is split into 30 volume archives for easier download and management. You can download all files or select specific volumes based on your needs.
Download Options
Option 1: Download All Files (Recommended for complete dataset)
# Download all 30 volume archives (31.1 GB total)
wget https://zenodo.org/api/records/17430550/files-archive -O biomedical_dataset_all.zip
Option 2: Download Individual Volumes
Each volume is independent and contains a subset of the dataset folders:
| Volume | Size | Contains Folders | Download Link |
|---|---|---|---|
| Part 01 | 23.2 MB | index_0001 ~ index_0011 | Download |
| Part 02 | 15.5 MB | index_0012 ~ index_0022 | Download |
| Part 03 | 33.7 MB | index_0023 ~ index_0033 | Download |
| Part 04 | 150.2 MB | index_0034 ~ index_0044 | Download |
| Part 05 | 26.9 MB | index_0045 ~ index_0055 | Download |
| Part 06 | 89.4 MB | index_0056 ~ index_0066 | Download |
| Part 07 | 14.0 GB | index_0067 ~ index_0077 | Download |
| Part 08 | 936.7 MB | index_0078 ~ index_0088 | Download |
| Part 09 | 15.8 GB | index_0089 ~ index_0099 | Download |
| Part 10 | 34.2 MB | index_0100 ~ index_0110 | Download |
| ... | ... | ... | See full list |
For the complete list of all 30 volumes, visit the Zenodo repository.
Extraction
After downloading, extract the archives:
# Extract a single volume
unzip biomedical_dataset_part01_of_30.zip
# Extract all volumes to the same directory (Linux/Mac)
for file in biomedical_dataset_part*.zip; do unzip "$file"; done
# Extract all volumes (Windows PowerShell)
Get-ChildItem biomedical_dataset_part*.zip | ForEach-Object { Expand-Archive $_ -DestinationPath . }
All volumes will extract to a downloaded_files/ directory containing folders index_0001 through index_0327.
Note: A complete list of required files is also provided in dataset_files_list.txt.
Usage
import json
from datasets import Dataset
# Load the dataset
with open('biomed_aqa_hf_dataset.json', 'r', encoding='utf-8') as f:
data = json.load(f)
# Convert to HuggingFace Dataset
dataset = Dataset.from_list(data)
# Access a sample
sample = dataset[0]
print(f"Question: {sample['question']}")
print(f"Task: {sample['task']}")
print(f"Files: {sample['files']}")
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