BOL: Related items

Google Genomics

Tenzin Paul — Thu, 18 Dec 2014 11:05:42 -0600

Explore genetic variation interactively. Compare entire cohorts in seconds with SQL-like queries. Compute transition/transversion ratios, genome-wide association, allelic frequency and more.
Process big genomic data easily. Run batch analyses like principal component analysis and Hardy-Weinberg equilibrium on as many samples as you like, in minutes or hours, with just a little code.
Use Google's infrastructure and big data expertise. Store one genome or a million using Google Genomics and take advantage of the same infrastructure that powers Search, Maps, YouTube, Gmail and Drive.
Support emerging global standards. 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/

Genome Stability Laboratory

Mon, 07 Mar 2016 04:16:32 -0600

The bakers yeast, Saccharomyces cerevisiae is an ideal model organism to understand mechanisms of meiotic chromosome segregation. In S. cerevisiae and in mammals, the majority of meiotic crossovers are formed through a highly conserved MSH4p-MSH5p, MLH1p-MLH3p dependent pathway. We are interested in charactering the role of these complexes in crossover formation and distribution among all homolog pairs. Errors in this process are linked to congenital birth defects in humans such as Down's syndrome.Our laboratory is also interested in understanding the effect of genetic background on mutation rate variation using S. cerevisiae as a model. These studies are relevant for understanding cancer progression, genome evolution and architecture. We use high- throughput genomic methods as well as classical genetics to achieve these aims.

More at http://faculty.iisertvm.ac.in/~nishantkt/index.html

MAKER

Jitendra Narayan — Sun, 07 Feb 2016 15:59:24 -0600

MAKER is a portable and easily configurable genome annotation pipeline.Its purpose is to allow smaller eukaryotic and prokaryotic genome projects to independently annotate their genomes and to create genome databases. MAKER identifies repeats, aligns ESTs and proteins to a genome, produces ab-initio gene predictions and automatically synthesizes these data into gene annotations having evidence-based quality values.

More at http://www.yandell-lab.org/software/maker.html

Address of the bookmark: http://www.yandell-lab.org/software/maker.html

Advanced Bash-Scripting Guide

Jit — Thu, 18 Feb 2016 04:50:51 -0600

This tutorial assumes no previous knowledge of scripting or programming, yet progresses rapidly toward an intermediate/advanced level of instruction . . . all the while sneaking in little nuggets of UNIX® wisdom and lore. It serves as a textbook, a manual for self-study, and as a reference and source of knowledge on shell scripting techniques. The exercises and heavily-commented examples invite active reader participation, under the premise that the only way to really learn scripting is to write scripts.

This book is suitable for classroom use as a general introduction to programming concepts.

More at http://tldp.org/LDP/abs/html/

Address of the bookmark: http://tldp.org/LDP/abs/html/

YASS :: genomic similarity search tool

Jit — Mon, 02 May 2016 09:26:00 -0500

YASS is a genomic similarity search tool, for nucleic (DNA/RNA) sequences in fasta or plain text format (it produces local pairwise alignments). Like most of the heuristic pairwise local alignment tools for DNA sequences (FASTA, BLAST, PATTERNHUNTER, BLASTZ/LASTZ, LAST ...), YASS uses seeds to detect potential similarity regions, and then tries to extend them to local alignments. This genomic search tool uses multiple transition constrained spaced seeds that enable to search more fuzzy repeats, as non-coding DNA/RNA. Another simple, but interesting feature is that you can specify the seed pattern used in the search step (as provided for example by iedera).

Main features of YASS are:

multiple, possibly overlapping seeds and a new hit criterion to ensure a good sensitivity/selectivity trade-off
transition-constrained spaced seeds to improve sensitivity (transition mutations are purine to purine [A<->G] or pyrimidine to pyrimidine [C<->T])
using different scoring schemes with bit-score and E-value evaluated according to the sequence background frequencies
parameterizable output filter for low complexity repeats
reporting of various alignment statistical parameters (mutation bias along triplets, transition/transversion)
post-processing step to group gapped alignments

Address of the bookmark: http://bioinfo.lifl.fr/yass/

4DGenome

Jitendra Narayan — Mon, 04 Jul 2016 00:44:55 -0500

Records in 4DGenome are compiled through comprehensive literature curation of experimentally-derived and computationally-predicted interactions. The current release contains 4,433,071 experimentally-derived and 3,605,176 computationally-predicted interactions in 5 organisms. Experimental data cover both high throughput datasets and individiual focused studies.

All interaction data are freely available in a standardized file format. Records can be queried by genomic regions, gene names, organism, and detection technology.

Address of the bookmark: http://4dgenome.research.chop.edu/

DECIPHER

Anjana — Fri, 30 Sep 2016 09:33:12 -0500

DECIPHER is a software toolset that can be used to maintain, analyze, and decipher large amounts of DNA sequence data. To install DECIPHER, see the Downloads page.

To begin using DECIPHER read the "Getting Started DECIPHERing" tutorial. Refer to the PDF documents below for instructions on how to use DECIPHER for various tasks.

Address of the bookmark: http://decipher.cee.wisc.edu/Documentation.html

GeneBreak: a tool to systematically identify genes recurrently affected by the genomic location of chromosomal CNA-associated breaks by a genome-wide approach

Jit — Sat, 01 Oct 2016 15:15:29 -0500

Development of cancer is driven by somatic alterations, including numerical and structural chromosomal aberrations. Currently, several computational methods are available and are widely applied to detect numerical copy number aberrations (CNAs) of chromosomal segments in tumor genomes. However, there is lack of computational methods that systematically detect structural chromosomal aberrations by virtue of the genomic location of CNA-associated chromosomal breaks and identify genes that appear non-randomly affected by chromosomal breakpoints across (large) series of tumor samples. ‘GeneBreak’ is developed to systematically identify genes recurrently affected by the genomic location of chromosomal CNA-associated breaks by a genome-wide approach, which can be applied to DNA copy number data obtained by array-Comparative Genomic Hybridization (CGH) or by (low-pass) whole genome sequencing (WGS). First, ‘GeneBreak’ collects the genomic locations of chromosomal CNA-associated breaks that were previously pinpointed by the segmentation algorithm that was applied to obtain CNA profiles. Next, a tailored annotation approach for breakpoint-to-gene mapping is implemented. Finally, dedicated cohort-based statistics is incorporated with correction for covariates that influence the probability to be a breakpoint gene. In addition, multiple testing correction is integrated to reveal recurrent breakpoint events. This easy-to-use algorithm, ‘GeneBreak’, is implemented in R (www.cran.r-project.org) and is available from Bioconductor (www.bioconductor.org/packages/release/bioc/html/GeneBreak.html).

Address of the bookmark: http://www.bioconductor.org/packages/release/bioc/html/GeneBreak.html

EAGER

Jit — Sat, 10 Dec 2016 18:07:23 -0600

The automated reconstruction of genome sequences in ancient genome analysis is a multifaceted process.

EAGER encompasses both state-of-the-art tools for each step as well as new complementary tools tailored for ancient DNA data within a single integrated solution in an easily accessible format.

https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0918-z

Address of the bookmark: https://github.com/apeltzer/EAGER-GUI

LAST

Bulbul — Mon, 19 Dec 2016 14:07:53 -0600

LAST can:

Handle big sequence data, e.g:
- Compare two vertebrate genomes
- Align billions of DNA reads to a genome
Indicate the reliability of each aligned column.
Use sequence quality data properly.
Compare DNA to proteins, with frameshifts.
Compare PSSMs to sequences
Calculate the likelihood of chance similarities between random sequences.
Do split and spliced alignment.
Train alignment parameters for unusual kinds of sequence (e.g. nanopore).

Address of the bookmark: http://last.cbrc.jp/