BOL: Related items

alien_index : identify potential contaminants or horizontally transferred genes in transcriptomes.

Jit — Sun, 02 Feb 2020 13:51:31 -0600

identify potential contaminants or horizontally transferred genes in transcriptomes.

algorithm is based on alogorithm described in the following: Gladyshev, Eugene A., Matthew Meselson, and Irina R. Arkhipova. "Massive horizontal gene transfer in bdelloid rotifers." science 320.5880 (2008): 1210-1213.

alien_index

Address of the bookmark: https://github.com/josephryan/alien_index

BIGMAC : breaking inaccurate genomes and merging assembled contigs for long read metagenomic assembly

Jit — Mon, 22 May 2017 05:43:51 -0500

This tool is for users to upgrade their metagenomics assemblies using long reads. This includes fixing mis-assemblies and scaffolding/gap-filling. If you encounter any issues, please contact me at kklam@eecs.berkeley.edu. My name is Ka-Kit Lam.

https://github.com/kakitone/MetaFinisherSC

https://github.com/kakitone/BIGMAC

Address of the bookmark: https://github.com/kakitone/BIGMAC

Bioinformatics tools to detect horizontal gene transfer (HGT) in genomes

Jit — Fri, 02 Mar 2018 04:56:23 -0600

Horizontal gene transfer (HGT), the “non-sexual movement of genetic material between two organisms” , is relatively common in prokaryotes and single-celled eukaryotes, but a number of factors combine to make it far rarer in multicellular eukaryotes. In order for a eukaryotic species to gain a gene by HGT, foreign DNA must enter the host nucleus, integrate into the genome, and in more complex organisms it must enter the sequestered germline in order to be transmitted to offspring. Once there, it must not experience strong negative selection, despite potential for genetic incompatibility with the host genome and mismatch between the niche of the donor and the host. Over the longer term, foreign DNA may become “domesticated” in the recipient genome and provide novel function.

Following are the popular tool to detect HGT in genomes:

T-REX / 3.22

HGT detection / download & compile

20525630

RANGER-DTL / 2.0

HGT detection / download binary

22689773

PhyloNet / 3.6.1

HGT detection / download binary

18662388

Jane / 4.01

HGT detection / download binary (!license!)

20181081

TREE-PUZZLE / 5.3.rc16

HGT detection / download & compile

11934758

CONSEL / 0.20

HGT detection / download

11751242

DarkHorse / 1.5 rev170

HGT detection / download & install

17274820

HGTector / 0.2.1

HGT detection / git clone

25159222

EGID / 1.0

HGT detection / download

22355228

GeneMarkS / 4.30

HGT detection / download binary (!license!)

9461475

fastv - detect virus

Jit — Sat, 11 Dec 2021 08:04:10 -0600

fastv is an ultra-fast tool for identification of SARS-CoV-2 and other microbes from sequencing data. It detects microbial sequences from FASTQ data, generates JSON reports and visualizes the result in HTML reports. This tool can be used to detect viral infectious diseases, like COVID-19. This tool supports both short reads (Illumina, BGI, etc.) and long reads (ONT, PacBio, etc.)

Address of the bookmark: https://github.com/OpenGene/fastv

Jaeger : an accurate and fast deep-learning tool to detect bacteriophage sequences

LEGE — Sun, 31 Aug 2025 06:30:16 -0500

Jaeger is a tool that utilizes homology-free machine learning to identify phage genome sequences that are hidden within metagenomes. It is capable of detecting both phages and prophages within metagenomic assemblies.

Address of the bookmark: https://github.com/MGXlab/Jaeger

RNA-Seq differential expression work flow using DESeq2

BioStar — Mon, 23 Aug 2021 10:57:14 -0500

One of the aim of RNAseq data analysis is the detection of differentially expressed genes. The package DESeq2 provides methods to test for differential expression analysis.

Address of the bookmark: http://www.sthda.com/english/wiki/rna-seq-differential-expression-work-flow-using-deseq2

Introduction to programming. Write short programs that generate graphics and animation.

Ram Yash Pal — Thu, 14 Aug 2014 23:29:04 -0500

Introduction to programming. Write short programs that generate graphics and animation.

http://funprogramming.org/

BEAP: Blast Extension and Assembly Program

Shruti Paniwala — Mon, 11 Jun 2018 04:52:56 -0500

The Blast Extension and Assembly Program (BEAP) is a computer program that uses a short starting DNA fragment, often a EST or partial gene segment, as "primer", to recursively blast nucleotide databases in an attempt to obtain all sequences that overlaps, directly or indirectly, with the "primer" therefore help to "extend" the length of the original sequence for constructing a "full length" sequence for functional analysis, or at least to obtain neighboring regions of the segment for SNP discovery and linkage disequilibrium analysis. The confidence of assembling the resulting sequences is achieved by using a known genome, such as human genome, as a reference. https://www.animalgenome.org/tools/beap/

Address of the bookmark: https://www.animalgenome.org/tools/beap/

Charpak exchange program

Fri, 10 Nov 2023 02:33:38 -0600

The scholarship is designed for Indian students from all fields and streams of study, enrolled in an Indian institution at the Bachelors or Master’s degree level, who wish to undertake a study exchange semester programme in France (for a period of one to six months).

BENEFITS
The Charpak exchange program offers the following benefits to the awardees based on merit: monthly stipend of 860 euros social security student visa and Campus France fee waiver assistance in finding an affordable student accommodation (subject to availability)

https://www.inde.campusfrance.org/charpak-exchange-scholarship-spring-session-jan-june

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