BOL: Related items

NgAgo challenge CRISPR !!

Abhimanyu Singh — Tue, 17 May 2016 03:31:32 -0500

A recent Nature Biotechnology paper from Chunyu Han’s lab, DNA-guided genome editing using the Natronobacterium gregoryi Argonaute, is a must-read for genome editing folks who want to learn about NgAgo. Their team sums up NgAgo’s potential pluses this way (emphasis mine):

“The useful features of NgAgo for genome editing include the following.First, it has a low tolerance to guide–target mismatch. A single nucleotide mismatch at each position of the gDNA impaired the cleavage efficiency of NgAgo, and mismatches at three positions completely blocked cleavage in our experiments. Second, 5′ phosphorylated short ssDNAs are rare in mammalian cells, which minimizes the possibility of cellular oligonucleotides misguiding NgAgo.Third, NgAgo follows a ‘one-guide-faithful’ rule, that is, a guide can only be loaded when NgAgo protein is in the process of expression, and, once loaded, NgAgo cannot swap its gDNA with other free ssDNA at 37 °C. All of these features could minimize off-target effects. Finally, it is easy to design and synthesize ssDNAs and to adjust their concentration, which is difficult with the Cas9-sgRNA system, if the sgRNA is expressed from a plasmid and the normal dosage of an ssDNA guide is only ~1/10 of that of a sgRNA expression plasmid.

NgAgo might be a more orderly way and perhaps even simpler way to go about genome editing than CRISPR, but the jury is still out on that until there are more papers and data. The NgAgo edit efficiency at this preliminary stage of technology development seems very strong. See the pics below

Reference: http://www.nature.com/nbt/journal/vaop/ncurrent/full/nbt.3547.html

Creating Genetic Maps from GBS data

BioStar — Fri, 08 Sep 2023 06:31:24 -0500

Genetic map, as the name suggest is simply knowing the relative positions of specific sequences across the genome. There are various methods to generate them, but most popular method is to use a cross between the known parents and examining their progenies. These kinds of crosses to create specific group of individuals of known ancestry is called as mapping population. Many types of mapping population exist. Here we will use the data collected from a Recombinant Inbred Line (RIL) (through selfing) to create a genetic map.

Address of the bookmark: https://bioinformaticsworkbook.org/dataAnalysis/GenomeAssembly/GeneticMaps/creating-genetic-maps.html

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

RAMI: a tool for identification and characterization of phylogenetic clusters in microbial communities

Jit — Mon, 07 Aug 2017 18:49:27 -0500

RAMI, which clusters related nodes in a phylogenetic tree based on the patristic distance. RAMI also produces indices of cluster properties and other indices used in population and community studies on-the-fly.

Availability: RAMI is licensed under GNU GPL and can be run or downloaded from http://www.acgt.se/online.html.

Address of the bookmark: https://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btp051

PyParanoid: a pipeline for rapid identification of homologous gene families in a set of genomes

BioStar — Thu, 13 Aug 2020 10:06:19 -0500

PyParanoid is a pipeline for rapid identification of homologous gene families in a set of genomes - a central task of any comparative genomics analysis. The "gold standard" for identifying homologs is to use reciprocal best hits (RBHs) which depends on performing a all-vs-all sequence comparison, usually using BLAST, to determine homology. However, these methods are computationally expensive, requiring O(n2) resources to identify RBHs. This is problematic, as the modern deluge of sequencing data means that comparative genomics analyses could be performed on datasets of thousands of strains.

Address of the bookmark: https://github.com/ryanmelnyk/PyParanoid

bacLIFE: an automated genome mining tool for identification of lifestyle associated genes

BioStar — Fri, 15 Mar 2024 04:59:14 -0500

bacLIFE is a streamlined computational workflow that annotates bacterial genomes and performs large-scale comparative genomics to predict bacterial lifestyles and to pinpoint candidate genes, denominated lifestyle-associated genes (LAGs), and biosynthetic gene clusters associated with each lifestyle detected. This whole process is divided into different modules:

Clustering module Predicts, clusters and annotates the genes of every input genome
Lifestyle prediction Employs a machine learning model to forecast bacterial lifestyle or other specified metadata
Analitical module (Shiny app) Results from the previous modules are embedded in a user-friendly interface for comprehensive and interactive comparative genomics.

You can find the complete wiki here [https://github.com/Carrion-lab/bacLIFE/wiki/bacLIFE-wiki]

Address of the bookmark: https://github.com/Carrion-lab/bacLIFE

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