Analysis tools for next generation sequencing data

Chipster's NGS analysis tools are grouped in the categories listed below. Chipster also contains a built-in genome browser, allowing you to view reads and results in their genomic context.

1. Quality control
2. Preprocessing
3. Utilities
4. Matching genomic regions
5. Alignment
6. Variants
7. RNA-seq
8. miRNA-seq
9. ChIP- and DNase-seq
10. CNA-seq
11. 16S rRNA sequencing
12. Single-cell RNA-seq

Quality control
• Read quality with FastQC
• Quality statistics with FASTX
• Read quality statistics with PRINSEQ
• RNA-seq quality metrics with RseQC
• RNA-seq strandedness inference and inner distance estimation using RseQcC
• Collect multiple metrics from BAM
• PCA and heatmap of samples with DESeq2
• Heatmap for RNA-seq results
• Check FASTQC file for errors
• Combine reports using MultiQC


Preprocessing
• Trim reads with Trimmomatic
• Trim reads for several criteria with PRINSEQ
• Trim reads with FASTX
• Trim primers/adaptors with TagCleaner
• Predict primers/adaptors with TagCleaner
• Statistics for primers/adaptors with TagCleaner
• Filter reads for several criteria with PRINSEQ
• Filter reads for quality with FASTX
• Filter reads for adapters, length and Ns with FASTX


Utilities
• Define NGS experiment
• Filter table by column term
• Filter table by column value
• Filter BED by column value
• Extract samples from table
• Table converter
• Sort table by column
• Join tables
• Modify text
• Count rows
• Preprocess count table
• Normalize count table
• Annotate Ensembl identifiers
• Plot normalized counts for a gene
• Index BAM
• Sort and index BAM
• Convert SAM to BAM, sort and index
• Make a subset of BAM
• Merge BAM
• Count alignments per chromosome in BAM
• Count alignment statistics for BAM
• Count alignments in BAM
• Remove duplicate reads from BAM
• Create consensus sequence from BAM
• Retrieve unique alignments from BAM
• Convert BAM to BED
• Convert FASTQ to FASTA
• Index FASTA
• Make a subset of FASTQ
• Rename reads in FASTQ
• Remove duplicate reads from FASTQ
• Sort BED
• Sort GTF
• Sort VCF
• Sort TSV
• FASTA from BED
• Extract features from BED
• Download file from url to server
• Retrieve FASTQ files from SRA database
• Retrieve BAM files from SRA database
• Retrieve sequences for a given organism in Ensembl
• Extract .gzip file
• Extract .gzip.tar file
• Compress a file with gzip
• Make a tar package
• List contents of a tar file
• A5 assembly pipeline for microbial genomes
• Make a list of file names
• VirusDetect
• VirusDetect with own host genome


Matching genomic regions
• Find overlapping regions
• Remove overlapping regions
• Combine region files
• Subtract BED
• Intersect BED
• Closest BED
• Window BED
• Merge BED
• Coverage BED
• Genome coverage BED
• Overlap BED
• Increase regions in BED
• Overlap BEDPE with BED
• Compare two BEDPE files


Alignment
• Bowtie2 for single end reads
• Bowtie2 for paired end reads
• Bowtie2 for single end reads and own genome
• Bowtie2 for paired end reads and own genome
• HISAT2 for single end reads
• HISAT2 for paired end reads
• HISAT2 for single end reads and own genome
• HISAT2 for paired end reads and own genome
• STAR for single end reads
• STAR for paired end reads
• BWA MEM for single or paired end reads
• BWA MEM for single or paired-end reads and own genome
• Bowtie for single-end reads
• Bowtie for paired-end reads
• Bowtie for single-end reads and own genome
• Bowtie for paired-end reads and own genome
• TopHat2 for paired end reads
• TopHat2 for single end reads
• TopHat2 for paired end reads and own genome
• TopHat2 for single end reads and own genome
• BWA-backtrack for single-end reads
• BWA-backtrack for paired-end reads
• BWA-backtrack for single-end reads and own genome
• BWA-backtrack for paired-end reads and own genome


Variants
• GATK4 -Call somatic SNVs and INDELs with Mutect2
• GATK4 -Create Somatic Panel of Normals
• GATK4 -Tabulate pileup metrics for inferring contamination
• GATK4 -Estimate contamination
• GATK4 -Filter Mutect2 calls
• GATK4 -Generate sequencing artifact metrics for filtering by orientation bias
• GATK4 -Filter Mutect2 calls by orientation bias
• Filter variants
• Annotate variants
• Ensembl variant effector predictor
• Intersect VCF files
• Calculate statistics on VCF file
• Call SNPs and short INDELs


RNA-seq
• Count aligned reads per genes with HTSeq
• Count aligned reads per genes with HTSeq using own GTF
• Count aligned reads per exons for DEXSeq
• Count aligned reads per exons for DEXSeq using own GTF
• Count reads per transcripts using eXpress
• Transform read counts
• Differential expression using DESeq2
• Differential expression using edgeR
• Differential expression using edgeR for multivariate experiments
• Differential exon expression using DEXSeq
• Assemble transcripts using Cufflinks
• Merge transcript assemblies with Cuffmerge
• Compare assembly to reference using Cuffcompare
• Differential expression using Cuffdiff
• Differential expression using Cuffdiff with replicates
• Hypergeometric test for ConsensusPathDB


Small RNA-seq
• Convert genomic BAM file to count table
• Convert miRBased BAM file to count table
• Differential expression using DESeq2
• Differential expression using edgeR
• Differential expression using edgeR for multivariate experiments
• Find miRNA targets
• GO enrichment for miRNA targets
• Correlate miRNA-seq and RNA-seq data
• Correlate miRNA-seq and gene expression array data
• Up-down analysis of miRNA target genes with array data
• Extract gene expression array data for miRNA targets
• VirusDetect
• VirusDetect with own host genome


ChIP- and DNase-seq
• Find peaks using MACS2
• Find peaks using MACS, treatment only
• Find peaks using MACS, treatment vs. control
• Find broad peaks using F-seq
• Find the nearest genes for regions
• Find unique and annotated genes
• GO enrichment for list of genes
• Find motifs with GADEM and match to JASPAR
• Dimont sequence extractor
• Dimont sequence extractor using own genome
• Find motifs with Dimont
• DimontPredictor


CNA-seq
• Define CNA-seq experiment
• Filter copy number bins
• Correct for GC content
• Normalize copy number data
• Plot copy number profiles
• Segment copy number data
• Call aberrations from segmented copy number data
• Identify common regions from called copy number data
• Add cytogenetic bands
• Count overlapping CNVs
• Plot copy number aberration frequencies
• Count the number of aberrations per sample
• Cluster called copy number data
• Plot correlations of called copy number data
• Group tests for called copy number data
• Survival test for called copy number data
• Plot survival curves for called copy number data
• Fuse regions by column value
• Fuse regions manually
• Detect genes from called copy number data
• GO enrichment for called gene copy numbers
• Match copy number and expression features
• Plot profiles of matched copy number and expression
• Test for copy number induced expression changes
• Plot copy number induced gene expression


16S rRNA sequencing
• Trim primers and barcodes and filter reads
• Combine paired reads to contigs
• Screen sequences for several criteria
• Extract unique sequences
• Align sequences to reference
• Filter sequence alignment
• Precluster aligned sequences
• Remove chimeric sequences
• Classify sequences to taxonomic units
• Produce count table and phenodata
• Statistical analysis for marker gene studies
• Summarize sequences
• Extract sequences from SFF file
• Split FASTQ file to FASTA and QUAL files
• Merge FASTA or QUAL files


Single-cell RNA-seq
• Seurat -Setup and QC
• Seurat -Filtering, regression and detection of variable genes
• Seurat -SCTransform: Filtering, regression and detection of variable genes
• Seurat -PCA
• Seurat -Clustering and detection of cluster marker genes
• Seurat -Visualize genes
• Seurat -Visualise features in UMAP plot
• Seurat -Combine two samples
• Seurat -Integrated analysis of two samples
• Seurat -Find conserved cluster markers and DE genes in two samples
• Seurat -Visualize genes with cell type specific responses in two samples
• Seurat BETA -Extract cells in a cluster
• Seurat BETA -Subset Seurat objects based on gene expression
• Seurat BETA -Compare clusters
• Preprocessing of DropSeq FASTQ files
• Merge aligned and unaligned BAM
• Estimate number of usable cells
• Create digital gene expression matrix