BOL: Related items

DeepVariant : an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data.

Jit — Sat, 25 Jan 2020 13:28:09 -0600

DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data.

DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data. DeepVariant relies on Nucleus, a library of Python and C++ code for reading and writing data in common genomics file formats (like SAM and VCF) designed for painless integration with the TensorFlow machine learning framework.

https://ai.googleblog.com/2017/12/deepvariant-highly-accurate-genomes.html

https://www.biorxiv.org/content/10.1101/092890v6

Address of the bookmark: https://github.com/google/deepvariant

ENCODE3: A collection of research articles and related content describing the Encyclopedia of DNA Elements, its datasets and tools.

Shruti Paniwala — Sat, 08 Aug 2020 08:25:21 -0500

How cells, tissues and organisms interpret the information encoded in the genome has vital implications for our understanding of development, health and disease. Launched in 2003, the ENCyclopedia Of DNA Elements (ENCODE) project has the aim of mapping the functional elements in the human genome (later expanded to include model organisms).

During the first phase of ENCODE, published in 2007, microarray-based technologies were used to detect regions associated with transcription factors, certain histone modifications and open chromatin within a pre-specified 1% of the human genome.

ENCODE’s second phase saw a switch to sequencing-based technologies, the addition of new assay types and the analysis of functional elements genome-wide, described in a collection of research articles in 2012.

The Encyclopedia paper of ENCODE 3, published in Nature, gives an overview of the various assays that were performed in human and mouse cell lines and tissues and describes a Registry of human and mouse candidate cis-regulatory elements (cCREs).

More at https://www.nature.com/immersive/d42859-020-00027-2/index.html

Metabuli 분리 improves metagenomic read classification

Abhi — Sat, 03 Jun 2023 20:15:04 -0500

Metabuli 분리 improves metagenomic read classification through metamers, DNA-AA k-mers, to be sensitive and specific, recovering 99% and 98% of DNA or AA classifiers.

Metabuli is metagenomic classifier that jointly analyze both DNA and amino acid (AA) sequences. DNA-based classifiers can make specific classifications, exploiting point mutations to distinguish close taxa. AA-based classifiers have higher sensitivity in detecting homology between query and reference sequences, leverageing higher conservation of AA sequences. Metabuli combines the information of both sequence types using a novel k-mer structure, metamer, to enable both specific and sensitive characterization of metagenomic samples. In addition, it can classify reads against a database of any size as long as it fits in the hard disk.

Address of the bookmark: https://github.com/steineggerlab/Metabuli

SVbyEye: R Package to visualize alignments between two or multiple DNA sequences

LEGE — Tue, 17 Sep 2024 02:34:57 -0500

R Package to visualize alignments between two or multiple DNA sequences including
a number of functionalities to facilitate processing of alignments in PAF format.

SVbyEye, an open-source R package to visualize and annotate sequence-to-sequence alignments along with various functionalities to process alignments in PAF format. The tool facilitates the characterization of complex SVs in the context of sequence homology helping resolve the mechanisms underlying their formation. Availability and implementation SVbyEye is available at https://github.com/daewoooo/SVbyEye.

Author: David Porubsky

Address of the bookmark: https://github.com/daewoooo/SVbyEye

DNA RNA MEME

Neel — Thu, 28 Jan 2021 11:23:14 -0600

Explain the DNA and RNA with picture ...

IgBLAST 1.17 is now available with improved identification of productive V gene sequences

Jit — Sun, 01 Nov 2020 16:52:58 -0600

A new release of IgBLAST (1.17), the popular package for classifying and analyzing immunoglobulin and T cell receptor sequences, is now available on the web and from the FTP site. The updated package is better at identifying productive V gene sequences. We added a new field , “V frame shift”, to the IgBLAST output to indicate whether the V gene translation frame contains a frame-shift. We have also updated the definition of a productive V(D)J sequence to now exclude those with internal frame shifts.

See the new IgBLAST manual on the NCBI GitHub site for more information on setting up and running IgBLAST.

If you have any questions or concerns, please email us at blast-help@ncbi.nlm.nih.gov

quarTeT: a telomere-to-telomere toolkit for gap-free genome assembly and centromeric repeat identification.

Abhi — Sat, 08 Jun 2024 15:54:36 -0500

quarTeT is a collection of tools for T2T genome assembly and basic analysis in automatic workflow.

Task include:

AssemblyMapper : reference-guided genome assembly
GapFiller : long-reads based gap filling
TeloExplorer : telomere identification
CentroMiner : centromere candidate prediction

https://academic.oup.com/hr/article/10/8/uhad127/7197191?login=false

Address of the bookmark: http://www.atcgn.com:8080/quarTeT/home.html

GRIDSS: the Genomic Rearrangement IDentification Software Suite

Rahul Nayak — Sun, 17 May 2020 10:27:44 -0500

GRIDSS is a module software suite containing tools useful for the detection of genomic rearrangements. GRIDSS includes a genome-wide break-end assembler, as well as a structural variation caller for Illumina sequencing data. GRIDSS calls variants based on alignment-guided positional de Bruijn graph genome-wide break-end assembly, split read, and read pair evidence.

Address of the bookmark: https://github.com/PapenfussLab/gridss

INC-Seq: accurate single molecule reads using nanopore sequencing

Jit — Mon, 27 Nov 2017 10:38:56 -0600

INC-Seq reads enabled accurate species-level classification, identification of species at 0.1 % abundance and robust quantification of relative abundances, providing a cheap and effective approach for pathogen detection and microbiome profiling on the MinION system.

Address of the bookmark: https://github.com/CSB5/INC-Seq

ARCS: scaffolding genome drafts with linked reads

Rahul Nayak — Tue, 06 Mar 2018 16:35:26 -0600

ARCS, an application that utilizes the barcoding information contained in linked reads to further organize draft genomes into highly contiguous assemblies. We show how the contiguity of an ABySS H.sapiensgenome assembly can be increased over six-fold, using moderate coverage (25-fold) Chromium data. We expect ARCS to have broad utility in harnessing the barcoding information contained in linked read data for connecting high-quality sequences in genome assembly drafts.

Address of the bookmark: https://github.com/bcgsc/ARCS/