BOL: Related items

Pathway Analysis

Rahul Agarwal — Fri, 03 Oct 2014 08:51:13 -0500

Pathway Analysis is usually performed with aim to enrich the genes with their functional information and reveal the underlying biological mechanisms pursue by genes. Pathway Analysis is not only limited to what biological pathways a particular set of expressed genes follow but also to disclose the relationships between these genes. With availability of more genomics, transcriptomics and proteomics data, interactions between genes involve in multiple pathways become more clear and also relationships between the genes, their transcripts, and their gene products. However, existing tools and dbs mainly based on knowledge driven approach in which pathways will be identified by finding the correlation between the information in one of the pathway knowledge databases (KEGG,Reactome,Panther,BioCarta, Panther,GO,NCI,WikiPathways,etc) and gene expression result for a specific conditions for instance tumor, obesity , cold resistant crops/plants, etc.

Introductory Articles/ppt/sources:

http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002375

http://bioinformatics.mdanderson.org/MicroarrayCourse/Lectures09/Pathway%20Analysis.pdf

http://gettinggeneticsdone.blogspot.de/2012/03/pathway-analysis-for-high-throughput.html

http://davetang.org/muse/tag/pathway/

https://www.biostars.org/p/42219/

http://bioinformatics.ca//files/public/Pathways_2014_Module4_v2.pdf

http://bioinformatics.ca//files/public/Pathways_2014_Module2.pdf

Impotant Database and Tools:

GeneMANIA, Cytoscape, IPA and Metacore (Commerical ), Pathway Commons, Reactome ,Panther, BioCyc, WikiPathways, Pathvisio, KEGG, NCI, Stringdb, Amigo, WebGestalt ,ConsensusPathDB ,GSEA,Blast2go

Popular R based tools:

Reactome.db, ReactomePA, ClusterProfiler, Gage, SPIA, topGO, Pathview,DOSE,GOStat

More:

http://www.bioconductor.org/help/search/index.html?q=Enrichment+analysis+

Integrative RNA and ChIP-Seq analysis of regulatory T-cells

Strand — Tue, 04 Aug 2015 05:03:19 -0500

Integrative RNA and ChIP-Seq analysis of regulatory T-cells , a Strand NGS application note describes how integrated multi-omics functionality in Strand NGS was used to find the regulatory role of FoxP3 in T-regulatory and T-helper cells. Learn how the gene expression profiles from RNA-Seq and FoxP3 DNA-protein binding sites from ChIP-Seq are integrated. For mor information, please write to us

Webinar on Unique Molecular Identifier (UMI)-powered Ultra-sensitive Variant Calling using Strand NGS - Case Study

Strand — Tue, 07 Nov 2017 03:55:52 -0600

Webinar on Unique Molecular Identifier-powered Ultra-sensitive Variant Calling using Strand NGS - Case Study

by Dr. Pandurang Kolekar, Bioinformatics Engineer, Strand Life Sciences

Abstract:

Unique Molecular Identifiers (UMIs) are short random nucleotide sequences that are increasingly being used in high-throughput sequencing experiments. In this webinar, we will highlight the UMI-friendly features of Strand NGS v3.1 including support for handling well known and customised UMI libraries, QC metrics, consensus alignment, UMI-based family size filters for read list, genome browser enabled with UMI-specific features and filters, UMI-aware variant calling parameters, and exporting UMI-tagged aligned samples. These all features together empower users to harness the potential of UMI-tagged NGS data for deeper insights. A case study demonstrating application of these UMI-based features in Strand NGS for low frequency variant calling in cfDNA sample will be presented.

UMI-tagged NGS libraries allow, ultra-sensitive detection of low frequency variants from liquid biopsy samples using DNA-Seq and accurate quantification of transcript-level expression using RNA-Seq. The recent release of Strand NGS v3.1, is equipped with the necessary features to efficiently analyse UMI-tagged NGS data helping researchers and labs involved in rare variant calling like in cfDNA based cancer diagnostics, and accurate transcript quantification with RNA-Seq.

Webinar Details:

Session 1: 13 Dec 2017, 2:30 PM IST
Session 2: 13 Dec 2017, 9:30 PM IST

Register here: http://www.strand-ngs.com/webinar_registration

RNA-Seq differential expression work flow using DESeq2

BioStar — Mon, 23 Aug 2021 10:57:14 -0500

One of the aim of RNAseq data analysis is the detection of differentially expressed genes. The package DESeq2 provides methods to test for differential expression analysis.

Address of the bookmark: http://www.sthda.com/english/wiki/rna-seq-differential-expression-work-flow-using-deseq2

Kallisto vs Salmon: Choosing the Right Tool for RNA-Seq Quantification

BioStar — Fri, 02 May 2025 06:28:46 -0500

In the world of transcriptomics, quantifying gene and transcript expression accurately and efficiently is crucial. With the explosion of RNA-Seq data, researchers have turned to fast, alignment-free tools that streamline the quantification process without compromising accuracy. Two leading tools in this space are Kallisto and Salmon. Both tools are highly efficient and widely used in the bioinformatics community, but they differ in subtle yet important ways. If you're unsure which one to use for your next RNA-Seq project, this post is for you.

What Are Kallisto and Salmon?

At their core, both Kallisto and Salmon are tools for quantifying transcript abundance from RNA-Seq reads. They bypass traditional alignment-based methods, replacing them with pseudoalignment or quasi-mapping, which drastically speeds up the process.

Kallisto was developed by Lior Pachter’s lab and introduced the concept of pseudoalignment using a de Bruijn graph.
Salmon, developed by Rob Patro’s group, builds on this idea with quasi-mapping and offers additional features like advanced bias correction.

Head-to-Head Comparison

1. Algorithm

Kallisto uses pseudoalignment, focusing on matching k-mers from reads to a transcriptome index.
Salmon uses quasi-mapping, which adds more flexibility and can also work with aligned reads (BAM files).

2. Input and Flexibility

Kallisto works with raw FASTQ reads and requires a custom transcriptome index.
Salmon accepts FASTQ or pre-aligned BAM files, giving you more workflow options.

3. Bias Correction

One of Salmon’s major advantages is its sophisticated bias correction system. It corrects for:

Sequence-specific bias
Positional bias
GC-content bias

Kallisto offers basic sequence bias correction but lacks the comprehensive models found in Salmon.

4. Speed and Resources

Kallisto is blazing fast and slightly more memory-efficient.
Salmon is still very fast, but the added features can come at a small computational cost.

5. Output and Downstream Analysis

Both tools provide transcript-level quantifications and support bootstrapping for variance estimation.
Salmon can also summarize counts at the gene level if provided with a mapping file (--geneMap).
Kallisto integrates seamlessly with Sleuth for differential expression analysis.
Salmon works well with tximport, DESeq2, edgeR, and other Bioconductor tools.

Choosing the Right Tool

Goal	Recommended Tool
Maximum speed	Kallisto
Advanced bias correction	Salmon
Use BAM files	Salmon
Transcript-level quantification with Sleuth	Kallisto
Integration with DESeq2/edgeR	Salmon

Example Command Lines

Kallisto (paired-end):

kallisto quant -i transcriptome.idx -o output -b 100 sample_R1.fastq sample_R2.fastq

Salmon (paired-end, bias correction):

salmon quant -i salmon_index -l A -1 sample_R1.fastq -2 sample_R2.fastq \
  -p 8 --validateMappings --seqBias --gcBias -o output

Conclusion

Both Kallisto and Salmon are exceptional tools that have transformed RNA-Seq analysis. Your choice largely depends on your priorities—whether it's speed, accuracy, flexibility, or compatibility with downstream tools.

For many users, Salmon offers a more complete and flexible solution, especially when bias correction and gene-level outputs are essential. However, Kallisto remains a favorite for quick, accurate quantification, especially when paired with the Sleuth pipeline.

transrate: Understanding your transcriptome assembly

Neel — Fri, 13 Jul 2018 07:49:26 -0500

Transrate is software for de-novo transcriptome assembly quality analysis. It examines your assembly in detail and compares it to experimental evidence such as the sequencing reads, reporting quality scores for contigs and assemblies. This allows you to choose between assemblers and parameters, filter out the bad contigs from an assembly, and help decide when to stop trying to improve the assembly.

Address of the bookmark: http://hibberdlab.com/transrate/index.html

Heap: a highly sensitive and accurate SNP detection tool for low-coverage high-throughput sequencing data

Jit — Thu, 19 Apr 2018 08:06:03 -0500

Heap, that enables robustly sensitive and accurate calling of SNPs, particularly with a low coverage NGS data, which must be aligned to the reference genome sequences in advance. To reduce false positive SNPs, Heap determines genotypes and calls SNPs at each site except for sites at the both end of reads or containing a minor allele supported by only one read. Performance comparison with existing tools showed that Heap achieved the highest F-scores with low coverage (7X) restriction-site associated DNA sequencing reads of sorghum and rice individuals. This will facilitate cost-effective GWAS and GP studies in this NGS era. Code and documentation of Heap are freely available from https://github.com/meiji-bioinf/heap and our web site (http://bioinf.mind.meiji.ac.jp/lab/en/tools.html).

Address of the bookmark: https://github.com/meiji-bioinf/heap

MSAProbs - Parallel and accurate multiple sequence alignment

Neel — Tue, 09 Jul 2019 23:58:44 -0500

MSAProbs is a well-established state-of-the-art multiple sequence alignment algorithm for protein sequences. The design of MSAProbs is based on a combination of pair hidden Markov models and partition functions to calculate posterior probabilities. Assessed using the popular benchmarks: BAliBASE, PREFAB, SABmark and OXBENCH, MSAProbs achieves statistically significant accuracy improvements over the existing top performing aligners, including ClustalW, MAFFT, MUSCLE, ProbCons and Probalign. In addition, MSAProbs is optimized for shared-memory CPUs by employing a multi-threaded design, and further parallelized for distributed-memory systems using MPI to overcome high memory overhead barrier and achieve good parallel and data-size scalability.

Address of the bookmark: http://msaprobs.sourceforge.net/homepage.htm#latest

dna2bit: an ultra-fast and accurate genomic distance estimation software

LEGE — Sun, 31 Aug 2025 06:24:58 -0500

dna2bit is a software tool developed in C++11, leveraging the capabilities of OpenMP for parallel computing and the popcount technique for efficient bit manipulation. It has been thoroughly tested using the g++ and clang compilers on both Linux and MacOS platforms.

Address of the bookmark: https://github.com/lijuzeng/dna2bit

RapClust: Accurate, Lightweight Clustering of de novo Transcriptomes using Fragment Equivalence Classes

Rahul Nayak — Thu, 04 Oct 2018 17:57:10 -0500

RapClust is a tool for clustering contigs from de novo transcriptome assemblies. RapClust is designed to be run downstream of the Sailfish or Salmon tools for rapid transcript-level quantification. Specifically, RapClust relies on the fragment equivalence classes computed by these tools in order to determine how seqeunce is shared across the transcriptome, and how reads map to potentially-related contigs across different conditions.

Address of the bookmark: https://github.com/COMBINE-lab/RapClust