Next-Generation Sequencing

Summary

This workflow is designed to help the user identify protein-DNA interactions from ChIP-Seq data. The user will provide a directory of files for analysis, and a file relating sample IDs to runs. The user will receive as output a summary of genome-wide protein-DNA interactions, including binding sites for transcription factors, histones, and other chromatin-associated proteins.

Methods

Reads are trimmed using TrimGalore, to remove low-quality (qual < 25) ends of reads and remove reads < 35bp. This workflow can be run with native open-source tools (NOST) or with Parabricks. With NOST, trimmed reads are aligned to a reference genome using BWA mem or Minimap2. Duplicate reads can optionally be marked using Picard MarkDuplicates. BAMs from the same sample generated by multiple runs are merged using Samtools. Alignment quality is assessed using FastQC, Samtools, and Bedtools. With Parabricks, trimmed reads are aligned, duplicate reads are marked and alignment quality is accessed using fq2bam. Quality metrics are summarized with MultiQC.

ChIP-Seq users can pick a workflow that uses MACS3 or DROMPAplus. In the MACS workflow, peaks are detected using MACS. Alignments are converted to BigWIG files. This deepTool computeMatrix is used to calculate scores per genome region and prepares an intermediate file that is used with plotHeatmap and plotProfiles. Typically, the genome regions are genes, but any other regions defined in a BED file can be used. computeMatrix accepts multiple score files (bigWig format) and multiple region files (BED format). This tool can also be used to filter and sort regions according to their score. In the DROMPAplus workflow, alignments are converted to BigWig, and peaks are detected using DROMPAplus.

Summary

This workflow is designed to help the user measure chromatin accessibility from ATAC-seq data. The user will provide a directory of files for analysis, and a file relating sample IDs to runs. The user will receive as output a summary of chromatin accessibility and nucleosome positioning in DNA.

Methods

Reads are trimmed using TrimGalore, to remove low-quality (qual < 25) ends of reads and remove reads < 35bp. This workflow can be run with native open-source tools (NOST) or with Parabricks. With NOST, trimmed reads are aligned to a reference genome using BWA mem or Minimap2. Duplicate reads can optionally be marked using Picard MarkDuplicates. BAMs from the same sample generated by multiple runs are merged using Samtools. Alignment quality is assessed using FastQC, Samtools, and Bedtools. With Parabricks, trimmed reads are aligned, duplicate reads are marked and alignment quality is accessed using fq2bam. Quality metrics are summarized with MultiQC.

Peak calling is run with MACS3. In the MACS workflow, peaks are detected using MACS. Alignments are converted to BigWIG files. This deepTool computeMatrix is used to calculate scores per genome region and prepares an intermediate file that is used with plotHeatmap and plotProfiles. Typically, the genome regions are genes, but any other regions defined in a BED file can be used. computeMatrix accepts multiple score files (bigWig format) and multiple region files (BED format). This tool can also be used to filter and sort regions according to their score.

Summary

This workflow is designed to help the user identify the presence and activity of regulatory elements in the genome. The user will provide input sequencing files, and receive as output an analysis of regulatory elements.

Methods

Reads are trimmed using TrimGalore, to remove low-quality (qual < 25) ends of reads and remove reads < 35bp. This workflow can be run with native open-source tools (NOST) or with Parabricks. With NOST, trimmed reads are aligned to a reference genome using BWA mem or Minimap2. Duplicate reads can optionally be marked using Picard MarkDuplicates. BAMs from the same sample generated by multiple runs are merged using Samtools. Alignment quality is assessed using FastQC, Samtools, and Bedtools. With Parabricks, trimmed reads are aligned, duplicate reads are marked and alignment quality is accessed using fq2bam. Quality metrics are summarized with MultiQC. Peak calling is handled by Starrseaker, and peaks are called directly from alignments.

Summary

This workflow is designed to help the user analyze protein-DNA interactions from Cut and Tag data. The user will provide as input a directory of files for analysis and will receive as output information on all protein-DNA interactions, including weak or low-abundance interactions.

Methods

Reads are trimmed using TrimGalore, to remove low-quality (qual < 25) ends of reads and remove reads < 35bp. This workflow can be run with native open-source tools (NOST) or with Parabricks. With NOST, trimmed reads are aligned to a reference genome using BWA mem or Minimap2. Duplicate reads can optionally be marked using Picard MarkDuplicates. BAMs from the same sample generated by multiple runs are merged using Samtools. Alignment quality is assessed using FastQC, Samtools, and Bedtools. With Parabricks, trimmed reads are aligned, duplicate reads are marked and alignment quality is accessed using fq2bam. Quality metrics are summarized with MultiQC.

Peak calling is run with MACS3. In the MACS workflow, peaks are detected using MACS. Alignments are converted to BigWIG files. This deepTool computeMatrix is used to calculate scores per genome region and prepares an intermediate file that is used with plotHeatmap and plotProfiles. Typically, the genome regions are genes, but any other regions defined in a BED file can be used. computeMatrix accepts multiple score files (bigWig format) and multiple region files (BED format). This tool can also be used to filter and sort regions according to their score.

Summary

This workflow is designed to help the user determine protein-DNA interactions from Cut and Run data. The user will provide a directory of files for analysis and will receive information on all protein-DNA interactions.

Methods

Reads are trimmed using TrimGalore, to remove low-quality (qual < 25) ends of reads and remove reads < 35bp. This workflow can be run with native open-source tools (NOST) or with Parabricks. With NOST, trimmed reads are aligned to a reference genome using BWA mem or Minimap2. Duplicate reads can optionally be marked using Picard MarkDuplicates. BAMs from the same sample generated by multiple runs are merged using Samtools. Alignment quality is assessed using FastQC, Samtools, and Bedtools. With Parabricks, trimmed reads are aligned, duplicate reads are marked and alignment quality is accessed using fq2bam. Quality metrics are summarized with MultiQC.

Peak calling is run with MACS3. In the MACS workflow, peaks are detected using MACS. Alignments are converted to BigWIG files. This deepTool computeMatrix is used to calculate scores per genome region and prepares an intermediate file that is used with plotHeatmap and plotProfiles. Typically, the genome regions are genes, but any other regions defined in a BED file can be used. computeMatrix accepts multiple score files (bigWig format) and multiple region files (BED format). This tool can also be used to filter and sort regions according to their score.

Summary

Reduced Representation Bisulfite Sequencing (RRBS) data is a widely used method for profiling DNA methylation. Bismark performs a series of steps to map RRBS reads to a reference genome, remove duplicate reads, and convert the DNA methylation data into a format that can be used for downstream analysis. Bismark also includes options for quality control, filtering, and normalization of the methylation data. Bismark requires a reference genome to map the sequencing reads.

Methods

This analysis was performed with the help of the Epigenomics: DNA Methylation Identification workflow on the Form Bio Platform. Reads are trimmed with Trim Galore [1]. Trimmed reads are aligned to a reference genome using Bismark [2] and Bowtie [3]. Reports and summaries are determined using scripts from Bismark.

Summary

Oxford nanopore sequencing technology (ONT) is a newer sequencing technology that offers advantages such as long reads and real-time sequencing. This data type can be used to detect methylation sites in DNA as methylated residues will carry a different charge than unmethylated residues. DeepSignal2 uses deep neural networks to detect DNA modifications, including DNA methylation and hydroxymethylation, directly from the raw nanopore sequencing data. It can identify modifications at single-nucleotide resolution and can be used to map modifications across the entire genome.

Methods

This analysis was performed with the help of the Epigenomics: DNA Methylation Identification workflow on the Form Bio platform. Raw Fast5s are first annotated before being associated with the input basecall to then be aligned to the genome using Tombo [4]. Once aligned methylation events and frequencies are determined using Deepsignal2 [5].

Summary

This workflow is designed to help the user determine differential gene abundances and differential expression between two or more groups of samples. The user will provide as input a folder containing all the read files needed for analysis and a sequencing file relating sample IDs to attributes. The user will receive as output differential gene and transcript abundance analysis files and comparison files between the two or more samples.

Methods

This analysis was performed using the Expression Analysis in RNASeq workflow on the Form Bio platform. Reads are trimmed using TrimGalore [1], to remove low quality (qual < 25) ends of reads and remove reads < 35bp. Trimmed reads are aligned to a reference genome using STAR [2] (default) or HiSAT2 [3]. Duplicate reads can optionally be marked using Picard MarkDuplicates. BAMs from the same sample generated by multiple runs are merged using Samtools [4]. The abundance of transcripts and genes are assessed using FeatureCount to generate raw gene counts [5], StringTie to generate FPKM [6] and Salmon to generate raw transcript counts [7]. Sample comparisons and differential gene/transcript expression analysis are performed using EdgeR [8], DESeq2 [9] and IsoformSwitchAnalyzeR [10].

Summary

This workflow is designed to help the user determine differentially expressed genes using abundance counts generated by previous workflow analysis. The user will provide a folder containing output files from previous analyses. The workflow will perform the statistical analysis again with a different composition of samples and output the results of this analysis.

Methods

Sample comparisons and differential gene/transcript expression analysis are performed using EdgeR [8], DESeq2 [9] and IsoformSwitchAnalyzeR [10].

Summary

This workflow is designed to help the user determine differential gene abundances and differential expression between two or more groups of human tumor samples. The user will provide as input a folder containing all read files needed for analysis and a sequencing file relating sample IDs to attributes. The user will receive as output differential gene and transcript abundance analysis files and comparison files between the two or more samples.

Methods

This analysis was performed using the Expression Analysis in RNASeq workflow on the Form Bio platform. Reads are trimmed using TrimGalore [1], to remove low quality (qual < 25) ends of reads and remove reads < 35bp. Trimmed reads are aligned to a reference genome using STAR [2] (default) or HiSAT2 [3]. Duplicate reads can optionally be marked using Picard MarkDuplicates. BAMs from the same sample generated by multiple runs are merged using Samtools [4]. The abundance of transcripts and genes are assessed using FeatureCount to generate raw gene counts [5], StringTie to generate FPKM [6] and Salmon to generate raw transcript counts [7]. Sample comparisons and differential gene/transcript expression analysis are performed using EdgeR [8], DESeq2 [9] and IsoformSwitchAnalyzeR [10]. Gene fusion events are detected using Star-Fusion [11]. Exon-skipping events are detected using RegTools [12].

Summary

This workflow is designed to help the user determine germline variants in diploid DNA. The user will provide input FastQ files containing the diploid DNA to be analyzed, and will recieve as output a summary of germline variants in the DNA compared to the chosen reference genome.

Methods

This analysis was performed using the Germline Variant Analysis workflow on the Form Bio platform. When the datatype is “Germline (Diploid)”, this workflow determines genetic variants including SNVs, insertions and deletions of high-quality NGS data when compared to a reference genome. Reads are trimmed using TrimGalore [1] or FastP [2], to remove low quality (qual < 25) ends of reads and remove reads < 35bp. These default value can be changed by the user. This workflow can be run with native open-source tools (NOST), Sentieon or with Parabricks.

With NOST and Sentieon, trimmed reads are aligned to a reference genome using BWA-MeM [3], Minimap2 [4] or Winnowmap [5] depending on data type. Duplicate reads can optionally be marked using Picard MarkDuplicates [6]. BAMs from the same sample generated by multiple runs are merged using Samtools [7]. Alignment qualtity is assessed using FastQC [8], Samtools [7], Bedtools [9] and Qualimap [10]. Variants can be detected with joint calling using Freebayes [11], Samtools/Bcftools [12], DNAScope and GATK4 [13].

With Parabricks, trimmed reads are aligned, duplicate reads are marked and alignment quality is accessed using fq2bam. Quality metrics are summarized with MultiQC. Variants can be detected with GATK [13] and DeepVariant [14] to produce gVCF files. Genotyping of gVCF files is determined using GLNexus [15]. Variants effects are determined using SNPEff [16].

Summary

This workflow is designed to help the user determine variants in ancient DNA. The user will provide input FastQ files containing the DNA to be analyzed, and will recieve as output a summary of variants in the DNA compared to the chosen reference genome.

Methods

This analysis was performed using the Germline Variant Analysis workflow on the Form Bio platform. When the datatype is “Ancient”,this workflow can be used to determine genetic variants of high quality NGS data in your project compared to a supported reference genome. Reads are trimmed using AdapterRemoval [17], FastP [2], or TrimGalore [1], to remove low quality (qual < 25) ends of reads and remove reads < 35bp. These default value can be changed by the user. Contaminates are detected using Kraken [18] with confidence of 0.8 using Kraken’s precompiled database or a custom database where the human genome has been removed. Unclassified trimmed reads are aligned to a reference genome using BWA MEM [3] or BWA Aln (with seed of 16,500, maximum edit distance of 0.01 and maximum gap opens of 2). BAMs from the same library generated by multiple runs are merged using Samtools [7]. Duplicate reads from the the same library can optionally be marked using PaleoMIX [19] or Picard MarkDuplicates [6]. BAMs from the same sample generated by multiple libraries are merged using Samtools [7]. Base recalibration is done using mapdamage2 [20]. Alignment quality is assessed using QualiMap [21], DamageProfiler [22], and MultiQC [23]. Germline variants can be detected using Freebayes [11], Samtools/Bcftools [12] and GATK4 [13]. In order to increase the speed of analysis, the Parabricks (requires GPUs) or Sentieon optimized versions of these algorithms are used for BWA Mem and GATK4. Genotyping of GVCF files is determined using GLnexus [15]. Variant effects are determined usng SNPEff [16].

Summary

This workflow is designed to help the user determine germline variants in small or synthetic genomes with an option to provide the custom genome sequence. The user will provide input FastQ files containing the DNA to be analyzed, and will recieve as output a summary of variants in the DNA compared to the chosen reference genome. It is assumed that Small genomes are haploid.

Methods

This analysis was performed using the Germline Variant Analysis workflow on the Form Bio platform. When data type is a “Small Genome”, this workflow can be used to determine genetic variants of high quality NGS data in your project compared to a supported reference genome. Reads are trimmed using TrimGalore [1]. Trimmed reads are aligned to a reference genome using BWA MEM [3]. Duplicate reads are marked using Picard MarkDuplicates [6]. Germline variants can be detected using Samtools/Bcftools [12]. In order to increase the speed of analysis, the Parabricks or Sentieon optimized versions of these algorithms can be used for BWA MEM. Variant effects are determined usng SNPEff [16] if the genome is provided by the platform. For SARS-CoV-2, genome sequences of samples are determined using BCFTools [12] and lineage classification is determined using PANGOLIN [24].

Summary

This workflow is designed to help the user determine variants in somatic DNA. The user will provide input FastQ files containing the DNA to be analyzed, and will receive as output a summary of variants in the DNA compared to the chosen reference genome.

Methods

When data type is “Somatic”, this workflow can be used to determine genetic variants of tumor NGS data compared to a supported reference genome. When a normal sample is provided, specialized somatic variant calling methods will be applied and allow users to filter germline variants from the resulting VCF files. Reads are trimmed using TTrimGalore [1] or FastP [2], to remove low quality (qual < 25) ends of reads and remove reads < 35bp. These default value can be changed by the user. Trimmed reads are aligned to a reference genome using BWA MEM [3], Minimap2 [4] or Winnowmap [5] depending on data type. Duplicates reads are marked using Picard MarkDuplicates [6]; if provided by Library. BAMs from the same sample generated by multiple runs are merged using Samtools [7]. Alignment quality is assessed using FastQC [8], Samtools [7], Bedtools [9] and Qualimap [10]. Quality reports are produced by MultiQC. Variant effects are determined using SNPEff [16]. Somatic variants can be detected in somatic or tumor-only mode using Strelka2 [25], Freebayes [11], DeepSomatic and MuTect2 [26], TNScope. In order to increase the speed of analysis, the Parabricks or Sentieon optimized versions of these algorithms are used for BWA Mem and GATK4. When a matched normal sample is present, tumor/normal germline SNP matching is confirmed using NGSCheckMate [27] and microsatellite stability is assessed using MSI-Sensor [28].