BOL: Related items

gapFinisher: A reliable gap filling pipeline for SSPACE-LongRead scaffolder output

Rahul Nayak — Thu, 14 May 2020 15:13:30 -0500

gapFinisher to process SSPACE-LongRead output to fill gaps after the scaffolding. gapFinisher is based on the controlled use of a previously published gap filling tool FGAP and works on all standard Linux/UNIX command lines.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6733440/

Address of the bookmark: https://github.com/kammoji/gapFinisher

Should you get sequenced? Not all bad genes predict disease

Rahul Agarwal — Thu, 29 Aug 2013 15:10:53 -0500

“What we really don’t know yet is whether the predictive aspects of the genome are going to turn out to be beneficial or potentially harmful”

“As we roll out genomic medicine we are fighting against this society-wide misconception that having the bad gene means you’re going to get the disease. That’s only true in a very few cases.”

Source:Today Health

Address of the bookmark: http://www.today.com/health/should-you-get-sequenced-not-all-bad-genes-predict-disease-8C11017154

Comparison of Short Read De Novo Alignment Algorithms

Rahul Agarwal — Wed, 21 Aug 2013 07:56:01 -0500

Excellent article to introduce different sequencing methods along with tools for de novo assembly of sequencing reads and their relevant references.

Title: Comparison of Short Read De Novo Alignment Algorithms

Author: Nikhil Gopal

Address of the bookmark: http://biochem218.stanford.edu/Projects%202011/Gopal%202011.pdf

RNA Bioinformatics and High Throughput Analysis Jena

Sat, 09 Nov 2013 20:03:56 -0600

Research Topics:

High Throughput Sequencing Analysis
Comparative Genomics
Identification and Annotation of Non-coding RNAs
Bioinformatic Analysis and System Biology of Viruses
Coevolution of Proteins and RNAs
Algorithmic Bioinformatics
Phylogenetic Analysis

http://www.rna.uni-jena.de/index.php

Bio-Rad Acquires GnuBIO

Rahul Agarwal — Sat, 19 Apr 2014 10:36:36 -0500

http://www.businesswire.com/news/home/20140411005331/en/Bio-Rad-Acquires-GnuBIO-Developer-Droplet-Based-DNA-Sequencing#.U1KXnPm1b8o

Deadly Human Pathogen Cryptococcus Sequenced

Rahul Agarwal — Fri, 25 Apr 2014 11:02:21 -0500

"Now, researchers have sequenced the entire genome and all the RNA products of the most important pathogenic lineage of Cryptococcus neoformans, a strain called H99. The results, which appear in PLOS Genetics, also describe a number of genetic changes that can occur after laboratory handling of H99 that make it more susceptible to stress, hamper its ability to sexually reproduce and render it less virulent."

Source:

http://www.biosciencetechnology.com/news/2014/04/deadly-human-pathogen-cryptococcus-fully-sequenced

Paper:

http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1004292

Drawback of Exome Sequencing

Rahul Agarwal — Mon, 02 Jun 2014 05:46:43 -0500

Dr Eric Londin, Assistant Professor, Thomas Jefferson University, USA, stated that analysis of 44 exome datasets from four different testing kits showed that they missed a high proportion of clinically relevant regions in the 56 ACMG genes. "At least one gene in each exome method was missing more than 40 percent of disease-causing genetic variants, and we found that the worst-performing method missed more than 90 percent of such variants in four of the 56 genes," he says.

Source: http://www.eurekalert.org/pub_releases/2014-05/esoh-pco052914.php

GraphMap - A highly sensitive and accurate mapper for long, error-prone reads

Rahul Agarwal — Mon, 06 Jul 2015 08:46:53 -0500

GraphMap is a novel mapper targeted at aligning long, error-prone third-generation sequencing data.
It is designed to handle Oxford Nanopore MinION 1d and 2d reads with very high sensitivity and accuracy, and also presents a significant improvement over the state-of-the-art for PacBio read mappers.

GraphMap was also designed for ease-of-use: the default parameters can handle a wide range of read lengths and error profiles, including: Illumina, PacBio and Oxford Nanopore.
This is an especially important feature for technologies where the error rates and error profiles can vary widely across, or even within, sequencing runs.

http://biorxiv.org/content/early/2015/06/10/020719

Address of the bookmark: https://github.com/isovic/graphmap

GRASS: a generic algorithm for scaffolding next-generation sequencing assemblies.

Abhimanyu Singh — Tue, 23 May 2017 05:20:32 -0500

GRASS (GeneRic ASsembly Scaffolder)-a novel algorithm for scaffolding second-generation sequencing assemblies capable of using diverse information sources. GRASS offers a mixed-integer programming formulation of the contig scaffolding problem, which combines contig order, distance and orientation in a single optimization objective. The resulting optimization problem is solved using an expectation-maximization procedure and an unconstrained binary quadratic programming approximation of the original problem. We compared GRASS with existing HTS scaffolders using Illumina paired reads of three bacterial genomes. Our algorithm constructs a comparable number of scaffolds, but makes fewer errors. This result is further improved when additional data, in the form of related genome sequences, are used.

Address of the bookmark: https://github.com/AlexeyG/GRASS

LRSDAY: Long-read Sequencing Data Analysis for Yeasts

Poonam Mahapatra — Mon, 26 Aug 2019 18:07:33 -0500

Long-read sequencing technologies have become increasingly popular in genome projects due to their strengths in resolving complex genomic regions. As a leading model organism with small genome size and great biotechnological importance, the budding yeast, Saccharomyces cerevisiae, has many isolates currently being sequenced with long reads.

Address of the bookmark: https://github.com/yjx1217/LRSDAY