BOL: Related items

HISAT2: a fast and sensitive alignment program for mapping next-generation sequencing reads

Rahul Nayak — Tue, 08 May 2018 04:27:22 -0500

HISAT2 is a fast and sensitive alignment program for mapping next-generation sequencing reads (both DNA and RNA) to a population of human genomes (as well as to a single reference genome). Based on an extension of BWT for graphs [Sirén et al. 2014], we designed and implemented a graph FM index (GFM), an original approach and its first implementation to the best of our knowledge. In addition to using one global GFM index that represents a population of human genomes, HISAT2 uses a large set of small GFM indexes that collectively cover the whole genome (each index representing a genomic region of 56 Kbp, with 55,000 indexes needed to cover the human population). These small indexes (called local indexes), combined with several alignment strategies, enable rapid and accurate alignment of sequencing reads. This new indexing scheme is called a Hierarchical Graph FM index (HGFM).

more at https://ccb.jhu.edu/software/hisat2/index.shtml

Address of the bookmark: https://github.com/infphilo/hisat2

Heatmapper: web-enabled heat mapping for all

Jit — Mon, 01 Oct 2018 08:34:41 -0500

Heatmapper is a freely available web server that allows users to interactively visualize their data in the form of heat maps through an easy-to-use graphical interface. Heatmapper is a versatile tool that allows users to easily create a wide variety of heat maps for many different data types and applications. Heatmapper allows users to generate, cluster and visualize:

1) expression-based heat maps from transcriptomic, proteomic and metabolomic experiments; 2) pairwise distance maps;

3) correlation maps;

4) image overlay heat maps;

5) latitude and longitude heat maps and

6) geopolitical (choropleth) heat maps.

Heatmapper offers a number of simple and intuitive customization options for easy adjustments to each heat map’s appearance and plotting parameters. Heatmapper also allows users to interactively explore their numeric data values by hovering their cursor over each heat map, or by using a searchable/sortable data table view.

Ref https://www.ncbi.nlm.nih.gov/pubmed/27190236

Address of the bookmark: http://www2.heatmapper.ca/

BlasR Mapping single molecule sequencing reads using Basic Local Alignment with Successive Refinement (BLASR): Theory and Application,

Jit — Wed, 23 May 2018 06:54:32 -0500

BLASR (Basic Local Alignment with Successive Refinement) for mapping Single Molecule Sequencing (SMS) reads that are thousands to tens of thousands of bases long with divergence between the read and genome dominated by insertion and deletion error.

Here is how I use the blasr to align PacBio reads to the contigs (target.fasta). The “target.fasta.sa” is the suffix array from “target.fasta” generated by sawriter.

blasr query.fa ./target.fasta -sa ./target.fasta.sa -bestn 40 -maxScore -500 -m 4 -nproc 24 -out target.m4 -maxLCPLength 15

the output format option “-m 4″ generate the alignment coordinate. Not fully documented, but I can explain that to you.

I use a 24 cores / 48G ram server for the alignment. It took about 2 to 3 hours aligning 3G PacBio Reads to 10^6 sequences of short read contigs with a mean 3.5kbp length.

Address of the bookmark: http://bix.ucsd.edu/projects/blasr/

FUMA GWAS: Functional Mapping and Annotation of Genome-Wide Association Studies

Jit — Sat, 01 Jun 2019 03:11:16 -0500

FUMA is a platform that can be used to annotate, prioritize, visualize and interpret GWAS results.
The SNP2GENE function takes GWAS summary statistics as an input, and provides extensive functional annotation for all SNPs in genomic areas identified by lead SNPs.
The GENE2FUNC function takes a list of gene IDs (as identified by SNP2GENE or as provided manually) and annotates genes in biological context

Address of the bookmark: https://fuma.ctglab.nl/

Molecular Genetics Lecture

Rahul Agarwal — Fri, 27 Sep 2013 04:24:45 -0500

"Robert Sapolsky makes interdisciplinary connections between behavioral biology and molecular genetic influences. He relates protein synthesis and point mutations to microevolutionary change, and discusses conflicting theories of gradualism and punctuated equilibrium and the influence of epigenetics on development theories."

"Robert Sapolsky is an American neuroendocrinologist, professor of biology, neuroscience, and neurosurgery at Stanford University, researcher and author" ----Wikipedia

Address of the bookmark: http://www.youtube.com/watch?v=_dRXA1_e30o

%MinMax: A versatile tool for calculating and comparing synonymous codon usage and its impact on protein folding.

Surabhi Chaudhary — Thu, 24 May 2018 02:53:31 -0500

%MM calculates whether a given gene sequence encodes amino acids using the most common codons possible, the least common codons possible, or (most typically) some combination of these extremes. See our PLoS ONE paper for more details on how the %MinMax algorithm works. %MinMax results are averaged over an 18-codon sliding window; hence the result for "codon window = 1" is the average codon usage for codons 1-18, codon window 2 = codons 2-19, etc.

Address of the bookmark: http://www.codons.org/

Internship program with ArrayGen Technolgies

Sun, 22 Jun 2014 23:18:31 -0500

Internship Program for Bioinformatics / Biotechnology Professionals Currently we offer positions to outstanding students interested in Next Generation Sequencing (NGS) data analysis. Applications are accepted throughout the year. Accepted students will be listed on web with their schedules. Accepted students can attend our future workshops and trainings freely at the specified venue.

Interested candidates may email their resume along with a cover letter to careers@arraygen.com

Official website: http://www.arraygen.com/

KOBAS: a web server for gene/protein functional annotation and functional gene set enrichment

Jit — Fri, 19 Oct 2018 09:36:11 -0500

KOBAS 3.0 is a web server for gene/protein functional annotation (Annotate module) and functional gene set enrichment(Enrichment module). For Annotate module, it accepts gene list as input, including IDs or sequences, and generates annotations for each gene based on multiple databases about pathways, diseases, and Gene Ontology. For Enrichment module, it can accept either gene list or gene expression data as input, and generates enriched gene sets, corresponding name, p-value or a probability of enrichment and enrichment score based on results of multiple methods.

Address of the bookmark: http://kobas.cbi.pku.edu.cn/

Caretta – A multiple protein structure alignment and feature extraction suite

Rahul Nayak — Fri, 18 Dec 2020 02:09:44 -0600

Caretta – a multiple protein structure alignment and feature extraction suite

Caretta, a multiple structure alignment suite meant for homologous but sequentially divergent protein families which consistently returns accurate alignments with a higher coverage than current state-of-the-art tools. Caretta is available as a GUI and command-line application and additionally outputs an aligned structure feature matrix for a given set of input structures, which can readily be used in downstream steps for supervised or unsupervised machine learning.

Address of the bookmark: http://www.bioinformatics.nl/caretta/

SOWDHAMINI Lab

Sun, 15 Sep 2013 09:19:12 -0500

Genome sequencing projects have enormous potential for benefiting human endeavors. However, just as acquiring a language's vocabulary does not enable one to speak it, databases that list the amino acid composition of proteins do not directly tell us much about these proteins' higher-level structure and function. The most productive way to indirectly exploit these databases has been to start with the small number of proteins that are fully-characterised and to assume that other "similar" proteins will have a related structure and function. Proteins with very similar amino acid sequence are "no-brainers", but the real test, which our group largely focuses on, is to detect the "essential" similarity in proteins whose non-critical sections have experienced random rearrangements during evolution. In such cases functionally similar proteins may have less than 25% sequence overlap.

More @ http://www.ncbs.res.in/sowdhamini/groups_sowdhamini.htm