BOL: Related items

mafTools

Radha Agarkar — Sat, 21 May 2016 22:40:21 -0500

Bioinformatics tools for dealing with Multiple Alignment Format (MAF) files.

Address of the bookmark: https://github.com/dentearl/mafTools

Understanding GO analysis

Neel — Wed, 08 Feb 2023 04:22:01 -0600

The confusion about gene ontology and gene ontology analysis can start right from the term itself. There are actually two different entities that are commonly referred to as gene ontology or “GO”:

the ontology itself, which is a set of terms with their precise definitions and defined relationships between them, and
the associations between gene products and GO terms, which are used to capture the existing knowledge about what each gene is known to do.

But the term gene ontology, or GO, is commonly used to refer to both, which is sometimes a source of potential confusion. In order to avoid this, here we will use the term “GO ontology” to describe the set of terms and their hierarchical structure and “GO annotations” to describe the set of associations between genes and GO terms.

There are 3 types of terms, or domains if you wish, in the gene ontology:

Biological Processes (BP)
Molecular Functions (MF)
Cellular Components (CC)

Address of the bookmark: https://advaitabio.com/faq-items/understanding-gene-ontology/

Project Assistant/Junior Research Fellow Position at Centre of Biomedical Research, Sanjay Gandhi Postgraduate Institute of Medical Sciences Campus, Lucknow.

Mon, 23 May 2016 01:31:29 -0500

Applications are invited from eligible candidates willing to join in a Department of Science and Technology (DST) project entitled “"Mapping neural regions involved in reading process in skilled adult Deaf reader: From neuroimaging perspective (functional Magnetic Resonance Imaging (fMRI) and Diffuse Tensor Imaging (DTI.)" as a Project Assistant/Junior Research Fellow (JRF) at Centre of Biomedical Research, Sanjay Gandhi Postgraduate Institute of Medical Sciences Campus, Lucknow.

Essential Qualification:

1. Master's degree in Bioinformatics / Computer Applications / Cognitive Science /Neuroscience/ Neuropsychology or equivalent. (Advantage will be given to NET JRF qualified candidate) Additional

Desirable Skills:

1) Programming skills (e.g., C++, Matlab, Python) 2) Experience/competent in working Window and Linux based programme.

Please mail your CV and covering letter to dr.uttam.kumar@gmail.com.

To know more about lab works please visit http://uttambrainlab.co.in/lab/.

Last date: 15/06/16

Advertisement: http://cbmr.res.in/wp-content/uploads/2016/05/Advertisement-19-5-2016.pdf

DIY Transcriptomics

Abhi — Wed, 31 Jul 2024 01:19:26 -0500

A semester-long course covering best practices for the analysis of high-throughput sequencing data from gene expression (RNA-seq) studies, with a primary focus on empowering students to be independent in the use of lightweight and open-source software using the R programming language and the Bioconductor suite of packages. This course follows a hybrid format in which online lectures are paired with in-person labs where students participate in hands-on, live coding exercises using real ‘omic datasets. The course is focused on datasets and topics central to infectious disease research, immunology, and One-Health, but the concepts and approaches covered are applicable to any genomic study.

https://diytranscriptomics.com

Address of the bookmark: https://diytranscriptomics.com

JRF Bioinformatics at ACTREC, Navi Mumbai

Mon, 30 May 2016 03:24:22 -0500

No. ACTREC/Advt./23/2016
JRF Bioinformatics recruitment in ACTREC (On contract Basis- Primeone Workforce Pvt. Ltd.)
Title : “Investigating the molecular basis of CaM/c-FLIP interaction to design specific c-FLIP inhibitor for modulating its anti-apoptotic function”.
Qualification : Master’s degree in bioinformatics, biochemistry, biotechnology and biological sciences from a recognized university with not less than 55% aggregate marks. Experience: Prior experience in modelling, protein-protein/ligand docking, Molecular dynamics simulation required. Knowledge in database development will be preferred.

Pay Scale : Rs. 32,500/-
How to apply
Candidates fulfilling these requirements should pre-register by sending their application in the prescribed format with recent CV and contact details of 2 referees by e-mail to ‘program.office@actrec.gov.in’ latest by 17.00 hrs on 23-06-2016. The interviews would be held on 27-06-2016 and only pre-registered candidates will be eligible to appear for interview. Candidates should report between 09.30 to 10.00 a.m. in Steno Pool, 3rd floor, Khanolkar Shodhika, ACTREC, Kharghar, Navi Mumbai.

More at http://www.actrec.gov.in/

PeGAS: A Comprehensive Bioinformatic Solution for Pathogenic Bacterial Genomic Analysis

LEGE — Mon, 01 Sep 2025 01:18:10 -0500

This is PeGAS, a powerful bioinformatic tool designed for the seamless quality control, assembly, and annotation of Illumina paired-end reads specific to pathogenic bacteria. This tool integrates state-of-the-art open-source software to provide a streamlined and efficient workflow, ensuring accurate insights into the genomic makeup of pathogenic microbial strains.

Address of the bookmark: https://github.com/liviurotiul/PeGAS

The Kingsley Lab

Fri, 03 Jun 2016 09:55:10 -0500

The Molecular Basis of Vertebrate Evolution. Naturally occurring species show spectacular differences in morphology, physiology, behavior, disease susceptibility, and life span. Although the genomes of many organisms have now been completely sequenced, Kingsley lab still know relatively little about the specific DNA sequence changes that underlie interesting species-specific traits. Kingsley lab laboratory is using a combination of genetic and genomic approaches to identify the detailed molecular mechanisms that control evolutionary change in vertebrates.

Single Cell RNAseq data analysis tutorial !!

Robert M Willioms — Mon, 27 Nov 2017 16:24:29 -0600

A major breakthrough (replaced microarrays) in the late 00’s and has been widely used since
Measures the average expression level for each gene across a large population of input cells
Useful for comparative transcriptomics, e.g. samples of the same tissue from different species
Useful for quantifying expression signatures from ensembles, e.g. in disease studies
Insufficient for studying heterogeneous systems, e.g. early development studies, complex tissues (brain)
Does not provide insights into the stochastic nature of gene expression

Following are the useful links:

Single Cell RNAseq data analysis Tutorial

A step-by-step workflow for low-level analysis of single-cell RNA-seq data

A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor

SCell: single-cell RNA-seq analysis software

https://github.com/diazlab/SCell

Beta-Poisson model for single-cell RNA-seq data analyses

https://github.com/nghiavtr/BPSC

Sincera: A Computational Pipeline for Single Cell RNA-Seq Profiling Analysis

https://research.cchmc.org/pbge/sincera.html

SC3 – consensus clustering of single-cell RNA-Seq data

http://biorxiv.org/content/early/2016/09/02/036558

Citrus: A toolkit for single cell sequencing analysis

http://biorxiv.org/content/early/2016/09/14/045070

Single-Cell Resolution of Temporal Gene Expression during Heart Development

http://www.cell.com/developmental-cell/fulltext/S1534-5807(16)30682-7

Scalable latent-factor models applied to single-cell RNA-seq data separate biological drivers from confounding effects

http://biorxiv.org/content/early/2016/11/15/087775

Single cell transcriptomes identify human islet cell signatures and reveal cell-type-specific expression changes in type 2 diabetes

http://genome.cshlp.org/content/early/2016/11/18/gr.212720.116.abstract

SCODE: An efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation

http://biorxiv.org/content/early/2016/11/21/088856

SCOUP is a probabilistic model to analyze single-cell expression data during differentiation

https://github.com/hmatsu1226/SCOUP

scLVM is a modelling framework for single-cell RNA-seq data

https://github.com/PMBio/scLVM

Selective Locally linear Inference of Cellular Expression Relationships (SLICER) algorithm for inferring cell trajectories

https://github.com/jw156605/SLICER

SinQC: A Method and Tool to Control Single-cell RNA-seq Data Quality

http://www.morgridge.net/SinQC.html

TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis

https://github.com/zji90/TSCAN

Visualization and cellular hierarchy inference of single-cell data using SPADE

http://www.nature.com/nprot/journal/v11/n7/full/nprot.2016.066.html

OEFinder: Identify ordering effect genes in single cell RNA-seq data

https://github.com/lengning/OEFinder

CovCal: Coverage / Read Count Calculator

Jit — Wed, 15 Jun 2016 18:08:13 -0500

Coverage / Read Count Calculator

Calculate how much sequencing you need to hit a target depth of coverage (or vice versa).

Instructions: set the read length/configuration and genome size, then select what you want to calculate.

Written by Stephen Turner, based on the Lander-Waterman formula, inspired by a similar calculator written by James Hadfield. Coverage is calculated as C=LN/G and reads as N=CG/L where C = Coverage (X),L = Read length (bp), G = Haploid genome size (bp), and N = Number of reads. Source code on GitHub.

Address of the bookmark: http://apps.bioconnector.virginia.edu/covcalc/

DarkHorse

Jit — Wed, 22 Jun 2016 05:37:38 -0500

DarkHorse is a bioinformatic method for rapid, automated identification and ranking of phylogenetically atypical proteins on a genome-wide basis. It works by selecting potential ortholog matches from a reference database of amino acid sequences, then using these matches to calculate a lineage probability index (LPI) score for each genome protein.

LPI scores are inversely proportional to the phylogenetic distance between database match sequences and the query genome. These scores are useful not only for large-scalede novo predictions of horizontally transferred proteins, but can also serve as an independent quality control test for potential horizontal transfer candidates identified by alternative methods, especially those based on nucleic acid signatures. Candidates having high LPI scores are unlikely to have been horizontally transferred, since they are highly conserved among closely related organisms.

One unique and powerful feature of the DarkHorse HGT Candidate database is the opportunity to explore the phylogenetic background of potential HGT donors as well as recipients. The breadth of the database allows not only query sequences, but also their database match partners to be evaluated for sequence similarity or novelty compared to taxonomically related organisms.

DarkHorse is configurable for varying degrees of phylogenetic granularity and protein sequence conservation. Users should consult the references cited below for a complete explanation of parameter selection and result interpretation. A brief tutorial page is also available on-line.

Address of the bookmark: http://darkhorse.ucsd.edu/download.html