BOL: Related items

Tools to Predict the Impact of Missense Variants !

Jit — Mon, 23 Apr 2018 12:57:33 -0500

Prioritizing missense variants for further experimental investigation is a key challenge in current sequencing studies for exploring complex and Mendelian diseases. A large number of in silico tools have been employed for the task of pathogenicity prediction, including PolyPhen‐2, SIFT, FatHMM, MutationTaster‐2, MutationAssessor, Combined Annotation Dependent Depletion, LRT, phyloP, and GERP++, as well as optimized methods of combining tool scores, such as Condel and Logit. Due to the wealth of these methods, an important practical question to answer is which of these tools generalize best, that is, correctly predict the pathogenic character of new variants.

Study of 10 tools on five datasets that such a comparative evaluation of these tools is hindered by two types of circularity: they arise due to (1) the same variants or (2) different variants from the same protein occurring both in the datasets used for training and for evaluation of these tools, which may lead to overly optimistic results. Comparative evaluations of predictors that do not address these types of circularity may erroneously conclude that circularity confounded tools are most accurate among all tools, and may even outperform optimized combinations of tools.

Following tools are useful for mis sense muation detection ...

PolyPhen‐2 (PP2)
“Predicts possible impact of an amino acid substitution on the structure and function of a human protein using straightforward physical and comparative considerations”

MutationTaster‐2 (MT2)
“Evaluation of the disease‐causing potential of DNA sequence alterations”

MutationAssessor (MASS)
“Predicts the functional impact of amino acid substitutions in proteins, such as mutations discovered in cancer or missense polymorphisms”

LRT
“Identify a subset of deleterious mutations that disrupt highly conserved amino acids within protein‐coding sequences, which are likely to be unconditionally deleterious”

SIFT
“Predicts whether an amino acid substitution affects protein function”

GERP++
“Identifies constrained elements in multiple alignments by quantifying substitution deficits. These deficits represent substitutions that would have occurred if the element were neutral DNA, but did not occur because the element has been under functional constraint. We refer to these deficits as “rejected substitutions.” Rejected substitutions are a natural measure of constraint that reflects the strength of past purifying selection on the element”

phyloP
“Compute conservation or acceleration P values based on an alignment and a model of neutral evolution”

FatHMM unweighted (FatHMM‐U)
Predicts “functional consequences of both coding variants, that is, nonsynonymous single‐nucleotide variants, and noncoding variants”

FatHMM weighted (FatHMM‐W)
Predicts “functional consequences of both coding variants, that is, nonsynonymous single‐nucleotide variants, and noncoding variants” and its weighting scheme attributes higher tolerance scores to SNVs in proteins, related proteins, or domains that already include a high fraction of pathogenic variantsh

Combined Annotation Dependent Depletion (CADD)
“CADD is a tool for scoring the deleteriousness of single‐nucleotide variants as well as insertion/deletions variants in the human genome”

ArrayGen Bioinformatics Genomics Group

Sun, 28 Sep 2014 14:09:55 -0500

ArrayGen is a global bioinformatics company which is a one stop solution for microarray designing and genomics data analysis. Our novel Array Design Approach Strategy (ADAS) aims to condense the time lag between demands of scientific community and manufacture industry, thereby expediting research processes.

ArrayGen specializes in Genomics data analysis and research, as we believe in the level of precision, predictability, benchmark-ability, and data analysis capability of genomics data over other forms of biological data. ArrayGen constantly strives to develop new solutions, and plug the existing gaps in the technological advancement of the field.

More http://www.arraygen.com/

MIX: Combining multiple assemblies from NGS data

Rahul Nayak — Tue, 08 May 2018 04:58:05 -0500

Mix is a tool that combines two or more draft assemblies, without relying on a reference genome and has the goal to reduce contig fragmentation and thus speed-up genome finishing. The proposed algorithm builds an extension graph where vertices represent extremities of contigs and edges represent existing alignments between these extremities. These alignment edges are used for contig extension. The resulting output assembly corresponds to a path in the extension graph that maximizes the cumulative contig length.

The Mix algorithm, approach and results were published in BMC bioinformatics : http://www.biomedcentral.com/1471-2105/14/S15/S16.

Address of the bookmark: https://github.com/cbib/MIX

Postdoc position in protein annotation and machine learning - Paris, France

Sat, 04 Oct 2014 08:10:45 -0500

We are interested in finding an excellent postdoc with interests in protein functional annotation, machine learning and computer grids. The position is open for 3.5 years at the Université Pierre et Marie Curie, in the heart of Paris.

Research topic: Protein function annotation, multiple probabilistic models, domain architecture, machine learning, combinatorial optimization, computer grid.

This project is run on the Laboratoire de Biologie Computationnelle et Quantitative UMR7238 CNRS-UPMC – Analytical Genomics team, headed by A.Carbone. It is co-advised with Pierre-Henri Wuillemin, Laboratoire d’Informatique de Paris 6 – Equipe DECISION.

The postdoc will be payed under a contract of Ingénieur de Recherche lasting 3.5 years and it is available from September 1st, 2014.

Group Web Page: http://www.lcqb.upmc.fr/AnalGenom/home.html

Ref. E-Mail: Alessandra Carbone alessandra.carbone@lip6.fr

vcfR: a package to manipulate and visualize VCF data in R

Jit — Thu, 25 Oct 2018 09:05:59 -0500

VcfR is an R package intended to allow easy manipulation and visualization of variant call format (VCF) data. Functions are provided to rapidly read from and write to VCF files. Once VCF data is read into R a parser function extracts matrices from the VCF data for use with typical R functions. This information can then be used for quality control or other purposes. Additional functions provide visualization of genomic data. Once processing is complete data may be written to a VCF file or converted into other popular R objects (e.g., genlight, DNAbin). VcfR provides a link between VCF data and the R environment connecting familiar software with genomic data.

Address of the bookmark: https://github.com/knausb/vcfR

JRF/SRF at Jawaharlal Nehru Institute ofAdvanced Studies (JNIAS), Hyderabad

Fri, 31 Oct 2014 08:48:23 -0500

Applications for Academic Projects in Biotechnology, Bioinformatics, Environmental Sciences and Computer Science & Engineering

About JNIAS
Jawaharlal Nehru Institute of Advanced Studies (JNIAS), Hyderabad has been established by Dr. D. Swaminadhan Research Foundation (DSRF), Hyderabad as a Research and Educational Institution with a view to contribute in developing advanced technologies and build „core competence‟ in specific areas. The activities of JNIAS involves: Education, Research Training and Innovations in the fields of Sciences, Technologies, Humanities and Social Sciences. It aims to blossom into an Advanced Institute of education and research with a reservoir of expertise and experience in the relevant fields and the necessary capability to harness multi-disciplinary research and studies. JNIAS has been recognized as an Advanced Research Institute by Jawaharlal Nehru Technological University Hyderabad (JNTUH), Hyderabad and Jawaharlal Nehru Technological University Anantapur (JNTUA), for offering Ph.D., P.G M.Phil, P.G Diploma and Training Programmes in Sciences and Engineering & Technology.

Jawaharlal Nehru Architecture and Fine Arts University (JNAFAU) Hyderabad also recognized JNIAS for offering UG, PG degree in Architecture.

Projects & Facilities

JNIAS offers wide range of projects:

Biotechnology area:

Molecular Biology
Microbiology
Nanotechnology
Bioinformatics (Schrodinger Software)
In Silico studies & Drug Designing
Sequence analysis
Protein structure function studies

Registration
Tuition Fees: Interested students need to pay the following tuition fees:
1. Six Month’s Project: Rs. 20,000/-
2. Four Month’s Project: Rs. 15,000/-
3. Three Month’s Project: Rs. 10,000/-
4. One Month - Hands on Training : Rs. 8,000/-

For enquires call:
91-7893203414 (Biotechnology), 91-9949582263 (Environmental Sciences) 91-8977369305 (Computer Science)

Interested student may download the application from the website (www.jnias.in) and send the hard copy of the completed application forms and Curriculum Vitae along with the Demand Draft drawn on any nationalized Banks in favor of “The Registrar, JNIAS, Secunderabad”. Application forms can be sent through email to academicprojects@jnias.in

Address
Jawaharlal Nehru Institute of Advanced Studies (JNIAS)
6th Floor, Buddha Bhavan, M.G Road,
Secunderabad - 500 003
Andhra Pradesh, India
Tele/Fax: 040- 27541551; 27541553
Mobile: 08885541554
Web site: www.jnias.in

Brochure : https://drive.google.com/file/d/0B3zPwhgA-u-nU0dyMFd2OWcxNUpSTWNYc0xDSGs5UDI4UDNB/view?usp=sharing

Tools for Differential expression analysis

Abhi — Tue, 08 Nov 2022 03:40:33 -0600

apeglm - https://bioconductor.org/packages/release/bioc/html/apeglm.html

ashr - https://github.com/stephens999/ashr, https://cran.r-project.org/web/packages/ashr/index.html

consensusDE - https://bioconductor.org/packages/release/bioc/html/consensusDE.html

DESeq2 - https://bioconductor.org/packages/release/bioc/html/DESeq2.html

edgeR - https://bioconductor.org/packages/release/bioc/html/edgeR.html

limma - https://kasperdanielhansen.github.io/genbioconductor/html/limma.html https://bioconductor.org/packages/release/bioc/html/limma.html

MetaCycle - https://cran.r-project.org/web/packages/MetaCycle/index.html, https://github.com/gangwug/MetaCycle

RUVSeq - https://bioconductor.org/packages/release/bioc/html/RUVSeq.html

SARTools - https://github.com/PF2-pasteur-fr/SARTools

tximport - https://github.com/mikelove/tximport

Google Genomics

Reshma Khatun — Fri, 17 Oct 2014 02:14:14 -0500

Google Genomics provides an API to store, process, explore, and share DNA sequence reads, reference-based alignments, and variant calls, using Google's cloud infrastructure.

Store alignments and variant calls for one genome or a million.
Process genomic data in batch by running principal component analysis or Hardy-Weinberg equilibrium, in minutes or hours, by using parallel computing frameworks like MapReduce.
Explore data by slicing alignments and variants by genomic range across one or multiple samples -- for your own algorithms or for visualization; or interactively process entire cohorts to find transition/transversion ratios, allelic frequency, genome-wide association and more using BigQuery.
Share genomic data with your research group, collaborators, the broader community, or the public. You decide.

Google Genomics is implementing the API defined by the Global Alliance for Genomics and Health for visualization, analysis and more. Compliant software can access Google Genomics, local servers, or any other implementation.

Address of the bookmark: https://cloud.google.com/genomics/

Comprehensive list of visualization tools for biological pathways

Neel — Tue, 30 Jan 2018 06:01:31 -0600

The study of biological pathways is a key to understand the different processes inside a cell: proteins exert their function not in isolation but in a tightly controlled network of interactions and reactions. Activation of a pathway typically leads to a change of state in the cell. Pathways come in different flavors, depending on their functions in the cell – the three main types are metabolic pathways, gene regulatory pathways, and signaling pathways. These biological pathways and networks are not only an appropriate approach to visualize molecular reactions. They have also become one leading method in -omics data analysis and visualization.

Following are the comprehensive list of visualization tools for biological pathways:

BiNA

Drawings of metabolic networks supporting hiding of cofactors and drawing of chemical structures

http://bina.unipax.info/

BioTapestry

Interactive tool for building, visualizing and sharing gene regulatory network models over the web

http://www.biotapestry.org/

Caleydo

Visual analysis framework targeted at biomolecular data. Visualization of interdependencies between multiple datasets

http://www.caleydo.org/

CellDesigner

A modeling tool for biochemical networks

http://www.celldesigner.org/

Edinburgh Pathway Editor

Edit and draw pathway diagrams

http://epe.sourceforge.net/SourceForge/EPE.html

GenMAPP

Visualization of gene expression and other genomic data on maps representing biological pathways and groupings of genes

http://www.genmapp.org/

Ingenuity IPA

Data integration platform and manually annotated pathways

http://tinyurl.com/IngenuityPath

JDesigner

Graphical modeling environment for biochemical reaction networks

http://jdesigner.sourceforge.net/Site/JDesigner.html

KaPPA View

Plant pathways

http://kpv.kazusa.or.jp/

KEGG Atlas

Interactive Kyoto Encyclopedia of Genes and Genomes pathways

http://www.genome.jp/kegg/

Omix

Visualizing multi-omics data in metabolic networks

https://www.omix-visualization.com

PathVisio

Biological pathway analysis software that allows drawing, editing and analysis of biological pathways

http://www.pathvisio.org/

VitaPad

Application to visualize biological pathways and map experimental data to them

http://tinyurl.com/vitapad/

Web tools for pathways

ArrayXPath

Mapping and visualizing microarray gene-expression data and integrated biological pathway resources using SVG

http://tinyurl.com/ArrayXPath/

GEPAT

Integrated analysis of transcriptome data in genomic, proteomic and metabolic contexts

http://gepat.sourceforge.net/

iPath

Web-based tool for the visualization, analysis and customization of pathway maps

http://pathways.embl.de/

Kegg-Based Viewer

KEGG-based pathway visualization tool for complex high-throughput data

http://www.g-language.org/data/marray/

MapMan

User-driven tool that displays large datasets onto diagrams of metabolic pathways or other processes

http://mapman.gabipd.org/web/guest/mapman

MetPA

Analysis and visualization of metabolomic data within the biological context of metabolic pathways

http://metpa.metabolomics.ca

Omics Viewer

Data mapping on BioCyc pathways (collection of 5500 pathway/genome databases)

http://www.biocyc.org/

Pathway Explorer

Interactive Java drawing tool for the construction of biological pathway diagrams in a visual way and the annotation of the components and interactions between them

http://genome.tugraz.at/pathwayexplorer/pathwayexplorer_description.shtml

Pathway projector

Zoomable pathway browser using KEGG atlas and Google Maps API

http://www.g-language.org/PathwayProjector/

PATIKA

Integrated environment composed of a central database and a visual editor, built around an extensive ontology and an integration framework

http://www.cs.bilkent.edu.tr/~patikaweb/

Reactome SkyPainter

Visualization of over-represented pathways and reactions from gene lists

http://www.reactome.org/skypainter-2

WikiPathways

Wiki-based, open, public platform dedicated to the curation of biological pathways by and for the scientific community

http://www.wikipathways.org/

Research Scientist – National Institute of Cholera and Enteric Diseases

Wed, 22 Oct 2014 10:26:46 -0500

The following post is to be filled up on purely temporary basis under the project entitled "Second phase of Task Force Biomedical Informatics Center of ICMR" under Dr. Santasabuj Das, Scientist 'D' of this Institute:-

01. Scientist II 01
Essential: Ph.D. degree in Life Sciences from a recognized university along with a minimum of 2 years of research experience in Bioinformatics as evidenced by publications in the peer reviewed journals.

OR
Ph.D. degree in Bioinformatics from a recognized university.

OR
M.Sc. in Bioinformatics from a recognized university along with a minimum of 3 years of research experience in Bioinformatics as evidenced by publications in the peer reviewed journals.

Desirable:
Thorough Knowledge about In silico genome analysis and comparative genomics.
Experience with in silico identification of novel virulence factors of pathogens, host-pathogen interactions and Systems Biology.
Additional Postdoctoral research experience in relevant subjects from a recognized institutions.

Rs. 44,000/- p.m. (consolidated) plus 30% HRA

Below 40 years

Applications along with Bio-Data containing detail work experience and full list of publications may be sent via email tosantasabujdas@yahoo.com latest by October 27, 2014.

Short-listed candidates will be called via email for an interview to be held at the institute in the second week of November, 2014.

Advertisement: www.niced.org.in/placements.htm