BOL: Related items

List of bioinformatics workflow management tools !

Rahul Nayak — Sat, 20 Mar 2021 00:15:25 -0500

Here are list of Workflow Managers

BigDataScript – A cross-system scripting language for working with big data pipelines in computer systems of different sizes and capabilities. [ paper-2014 | web ]
Bpipe – A small language for defining pipeline stages and linking them together to make pipelines. [ web ]
Common Workflow Language – a specification for describing analysis workflows and tools that are portable and scalable across a variety of software and hardware environments, from workstations to cluster, cloud, and high performance computing (HPC) environments. [ web ]
Cromwell – A Workflow Management System geared towards scientific workflows. [ web ]
Galaxy – a popular open-source, web-based platform for data intensive biomedical research. Has several features, from data analysis to workflow management to visualization tools. [ paper-2018 | web ]
Nextflow (recommended) – A fluent DSL modelled around the UNIX pipe concept, that simplifies writing parallel and scalable pipelines in a portable manner. [ paper-2018 | web ]
Ruffus – Computation Pipeline library for python widely used in science and bioinformatics. [ paper-2010 | web ]
SeqWare – Hadoop Oozie-based workflow system focused on genomics data analysis in cloud environments. [ paper-2010 | web ]
Snakemake – A workflow management system in Python that aims to reduce the complexity of creating workflows by providing a fast and comfortable execution environment. [ paper-2018 | web ]
Workflow Descriptor Language – Workflow standard developed by the Broad. [ web ]

R.A Bioinformatics at JNU, Delhi

Tue, 10 May 2016 01:50:38 -0500

R.A Jobs opportunity in Jawaharlal Nehru University (JNU) on purely temporary basis

Project Title : "Structural and functional characterization of serine biosynthetic pathway enzymes from Entamoeba histolytica"

No. of Post : 01

Qualification : Ph.D in any computational biology / bioinformatics

Desirable : 1. Experience with docking, simulations and structural biology with proof of publications is preferred. 2. National Eligibility Test cleared [CSIR/UGC] candidate is preferred.

Salary : According to CSIR rules
How to apply

The application on plain paper indicating name, date of birth/age, address, essential/technical/professional qualifications. experience, list of publications should reach The Prof. S. Gourinath, Principal Investigator, [Project Director], School of Life Sciences, Jawaharlal Nehru University, New Delhi-110067, on or before 23rd May 2016.

More at http://www.jnu.ac.in/Career/currentjobs.htm

Pacasus: Correction of palindromes in long reads from PacBio and Nanopore

BioStar — Mon, 12 Nov 2018 05:26:48 -0600

Tool for detecting and cleaning PacBio / Nanopore long reads after whole genome amplification. Check the poster from the Revolutionizing Next-Generation Sequencing (2nd edition) conference in the source folder: https://github.com/swarris/Pacasus/blob/master/vib2017.pdf.

The prepint version is found on http://www.biorxiv.org/content/early/2017/08/09/173872

It uses the pyPaSWAS framework for sequence alignment (https://github.com/swarris/pyPaSWAS)

Address of the bookmark: https://github.com/swarris/Pacasus

ngs-bits - Short-read sequencing tools

Neel — Thu, 16 Jan 2020 23:14:00 -0600

Binaries of ngs-bits are available via Bioconda. Alternatively, ngs-bits can be built from sources:

Binaries for Linux/macOS
From sources for Linux/macOS
From sources for Windows

Address of the bookmark: https://github.com/imgag/ngs-bits

RNA-Seq Data Pathway and Gene-set Analysis Workflows

Jit — Fri, 25 Oct 2013 08:00:48 -0500

It describe the GAGE (Luo et al., 2009) /Pahview (Luo and Brouwer, 2013) workflows on RNA-Seq data pathway analysis and gene-set analysis. The gage package (2.12.0) now includes a new tutorial, “RNA-Seq Data Pathway and Gene-set Analysis Workflows“.

First cover a full workflow from preparation, reads counting, data preprocessing, gene set test, to pathway visualization in about 40 lines of codes. The same workflow can be used for GO analysis or other types of gene set analysis too. We also describe joint workflows, i.e. to do gene-level analysis using one of the major RNA-Seq analysis tools, DEseq/DEseq2, edgeR, limma and Cufflinks, and feed the results into GAGE/Pahview for pathway analysis or visualization. All these workflows are implemented in R/Bioconductor.

The work ows cover the most common situations and issues for RNA-Seq data pathway analysis. Issues like data quality assessment are relevant for data analysis in general yet out the scope of this tutorial. Although we focus on RNA-Seq data here, but pathway analysis work ow remains similar for microarray, particularly step 3-4 would be the same. Please check gage and pathview vigenttes for details.

Note: You need to update to current release versions of R(3.0.2)/ Bioconductor(2.13) to use all the features.

Reference:

Please check it out:
http://bioconductor.org/packages/release/bioc/html/gage.html
http://bioconductor.org/packages/release/bioc/vignettes/gage/inst/doc/RNA-seqWorkflow.pdf

BioJupies: Automatically Generates RNA-seq Data Analysis Notebooks

Rahul Nayak — Sun, 20 Dec 2020 11:43:45 -0600

With BioJupies you can produce in seconds a customized, reusable, and interactive report from your own raw or processed RNA-seq data through a simple user interface

BioJupies now supports user accounts! Sign in from the top right corner of the page for access to unlimited private notebooks, RNA-seq datasets and alignment jobs.

Address of the bookmark: https://amp.pharm.mssm.edu/biojupies/

Post-doctoral position / Scientist (m/f/d) in Bioinformatics

Mon, 15 Mar 2021 11:34:23 -0500

The Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V. in Dortmund is looking for a Post-doctoral position / Scientist (m/f/d) in Bioinformatics

More at

https://www.isas.de/files/redaktion/jobs/2021/18.60.1_Postdoc_deNBI_06_21_AS.pdf

https://www.isas.de/en/news/062021-post-doctoral-position-scientist-mfd-in-bioinformatics

RNA-seq workflow: gene-level exploratory analysis and differential expression

Jit — Sat, 09 Oct 2021 07:59:23 -0500

Here we walk through an end-to-end gene-level RNA-seq differential expression workflow using Bioconductor packages. We will start from the FASTQ files, show how these were quantified to the reference transcripts, and prepare gene-level count datasets for downstream analysis. We will perform exploratory data analysis (EDA) for quality assessment and to explore the relationship between samples, perform differential gene expression analysis, and visually explore the results.

Address of the bookmark: http://master.bioconductor.org/packages/release/workflows/vignettes/rnaseqGene/inst/doc/rnaseqGene.html

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+

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