BOL: Related items

TRITEX sequence assembly pipeline for Triticeae genomes

Jit — Tue, 20 Aug 2019 09:47:14 -0500

The pipeline is open-source and hosted in a public Bitbucket repository.

TRITEX has been run on highly inbred genotypes of barley (Hordeum vulgare), tetraploid wheat (Triticum turgidum) and hexaploid wheat (T. aestivum) with reasonable results: super-scaffold N50 values in the range of dozens of Mb and pseudomolecules with better gene space representation than a BAC-by-BAC assembly. It has never been tested and is not expected to work on heterozygous or autopolyploid genomes.

A protocol for generating chromosome-conformation capture sequencing (Hi-C) data suitable for use with the pipeline is described in Himmelbach et al. 2018. Refer to the technical notes of 10X Genomics on how to generate Chromium data.

Address of the bookmark: https://tritexassembly.bitbucket.io/

Kevler: Reference-free variant discovery in large eukaryotic genomes

Jit — Tue, 28 Jan 2020 03:21:53 -0600

Welcome to kevlar, software for predicting de novo genetic variants without mapping reads to a reference genome! kevlar's k-mer abundance based method calls single nucleotide variants (SNVs), multinucleotide variants (MNVs), insertion/deletion variants (indels), and structural variants (SVs) simultaneously with a single simple model.

More at https://kevlar.readthedocs.io/en/latest/

https://www.cell.com/iscience/pdf/S2589-0042(19)30259-7.pdf

Address of the bookmark: https://github.com/kevlar-dev/kevlar

PhiSpy: PhiSpy identifies prophages in Bacterial (and probably Archaeal) genomes

Jit — Tue, 30 Jun 2020 21:36:19 -0500

PhiSpy identifies prophages in Bacterial (and probably Archaeal) genomes. Given an annotated genome it will use several approaches to identify the most likely prophage regions.

Initial versions of PhiSpy were written by

Sajia Akhter (sajia@stanford.edu) Edwards Bioinformatics Lab

Improvements, bug fixes, and other changes were made by

Katelyn McNair Edwards Bioinformatics Lab and Przemyslaw Decewicz DEMB at the University of Warsaw

Address of the bookmark: https://github.com/linsalrob/PhiSpy

LoReTTA, a user-friendly tool for assembling viral genomes from PacBio sequence data

Neel — Wed, 23 Jun 2021 07:54:53 -0500

LoReTTA (Long Read Template-Targeted Assembler), a tool designed for performing de novo assembly of long reads generated from viral genomes on the PacBio platform. LoReTTA exploits a reference genome to guide the assembly process, an approach that has been successful with short reads.

https://academic.oup.com/ve/article/7/1/veab042/6248116

Address of the bookmark: https://academic.oup.com/ve/article/7/1/veab042/6248116

NGenomeSyn: an easy-to-use and flexible tool for publication-ready visualization of syntenic relationships across multiple genomes

LEGE — Tue, 10 Sep 2024 04:54:55 -0500

NGenomeSyn: an easy-to-use and flexible tool for publication-ready visualization of syntenic relationships across multiple genomes

NGenomeSyn [multiple (N) Genome Synteny], for publication-ready visualization of syntenic relationships of the whole genome or local region and genomic features (e.g. repeats, structural variations, genes) across multiple genomes with a high customization. NGenomeSyn provides an easy way for its users to visualize a large amount of data with a rich layout by simply adjusting options for moving, scaling, and rotation of target genomes. Moreover, NGenomeSyn could be applied on the visualization of relationships on non-genomic data with similar input formats.

https://academic.oup.com/bioinformatics/article/39/3/btad121/7072460

Address of the bookmark: https://github.com/hewm2008/NGenomeSyn

Ancestral sequence reconstruction steps !

Surabhi Chaudhary — Fri, 18 May 2018 08:28:26 -0500

Ancestral sequence reconstruction (ASR) – also known as ancestral gene/sequence reconstruction/resurrection – is a technique used in the study of molecular evolution. The method consists of the synthesis of an ancestral gene and expression of the corresponding ancestral protein. The idea of protein 'resurrection' was suggested in 1963 by Pauling and Zuckerkandl. Some early efforts were made in the eighties-nineties, led by the laboratory of Steven A. Benner, showing the potential of this technique – one that only started to be fulfilled in the post-genomic era. Thanks to the improvement of algorithms and of better sequencing and synthesis techniques, the method was developed further in the early 2000s to allow the resurrection of a greater variety of and much more ancient genes. Over the last decade, ancestral protein resurrection has developed as a strategy to reveal the mechanisms and dynamics of protein evolution.

BEAST is the best way to predict the ancestral structure. but, I suggest following steps?

1- Alignments "Mafft - http://mafft.cbrc.jp/alignment/software/source.html"

mafft --maxiterate 1000 --reorder --thread 24 --genafpair Dataset.fasta > Dataset_Alig.fasta

2- Your dataset has a good phylogenetic signal, is possible to perform with Tree-Puzzle "http://www.tree-puzzle.de";

3 - This dataset which the saturation index, I perform with "http://dambe.bio.uottawa.ca/dambe.asp";

4- Has evidence of possible recombination in your dataset, the evaluate if this presence or absence, because this may to influence the grouping of clades, I perform with

---recombination

4.1- Phi-test, implemented in SplitTree4"http://www.splitstree.org", (.nex file)

4.2- GARD deployed in webserver in the DataMonkey "http://www.datamonkey.org/" - turning to the amino acid seaview -> view proteins -> save as ...) Ideally do a tree-based groups.

4.3- RDP4 for download and installation on Windows in "http://web.cbio.uct.ac.za/~darren/rdp.html"

4.4- Hyphy (Mac, Windows, Linux) in "http://hyphy.org/w/index.php/Download"

4.5- Path-o-Gen (temporal structure of a tree input file -> arquivo.tre)

These steps above, I call of pre-processing to inferences phylogenetic...

5- Perform phylogenetic tree, used Bayesian Inference with Molecular Clock, but is necessary Clock Testing:

- This step is performed with program Beast (Beauti, Beast and TreeAnnotator), and Tracer_v1.5 more FigTree to inspection.

- Tutorials: http://beast.bio.ed.ac.uk/tutorials

- Downloads: http://beast.bio.ed.ac.uk/downloads

How DNA is Packaged (Advanced)

Mon, 23 Sep 2013 18:08:34 -0500

Each chromosome consists of one continuous thread-like molecule of DNA coiled tightly around proteins, and contains a portion of the 6,400,000,000 basepairs (DNA building blocks) that make up your DNA. Originally created for DNA Interactive ( http://www.dnai.org ). TRANSCRIPT: In this animation we'll see the remarkable way our DNA is tightly packed up to fit into the nucleus of every cell. The process starts with assembly of a nucleosome, which is formed when eight separate histone protein subunits attach to the DNA molecule. The combined tight loop of DNA and protein is the nucleosome. Six nucleosomes are coiled together and these then stack on top of each other. The end result is a fiber of packed nucleosomes known as chromatin. This structure, is then looped and further packaged using other proteins (which are not shown here) to give the final "chromosomal" shapes. It is this remarkable multiple folding which allows six feet of DNA to fit into the nucleus of each cell in our body. And a typical cell nucleus is so small that ten thousand could fit on the tip of a needle. It is important to realize that chromosomes are not always present, they form only when cells are dividing. At other times, as we can see here at the end of cell division, our DNA becomes less highly organized.)

Recombination detection tool

Jit — Tue, 02 Feb 2016 10:11:14 -0600

A program to detect recombination hotspots using population genetic data.

More at https://github.com/auton1/LDhot

Address of the bookmark: https://github.com/auton1/LDhot

MEDEA: Comparative Genomic Visualization with Adobe Flash

Jit — Tue, 26 Apr 2016 12:15:16 -0500

As the number of sequence and annotated genomes grows larger, the need to understand, compare, and contrast the data becomes increasingly important. Using the power of the human visual system to detect trends and spot outliers is necessary in such large and complex data sets.

More at http://www.broadinstitute.org/annotation/medea/

Address of the bookmark: http://www.broadinstitute.org/annotation/medea/

Ideoplot

Shruti Paniwala — Mon, 13 Feb 2017 09:47:32 -0600

Simple ideogram plotting and annotation in R.

Basic usage:

Rscript Ideoplot.R --heatmap hm.bed --annotate annotations.bed --out ideogram.pdf
-or-
Rscript Ideoplot.R --annotate annotations.bed

Options
  --ideobed, i      A bed file of reference contig lengths/chromosome names
  --heatmap, -h     Fill chromosomes with normalized heatmap
                   (described below)
  --annotate, -a    Add character annotations.
  --out, -o         PDF output name.
  --stripes, -s     Specify a file containing the layout of the
                    annotations (description below)
  --bars, -b        Add track annotations
  --reference, -f   Either hg19, or hg38
  --topdown, r      Flag, when set, flips the orientation (P arms
                    drawn on top).

Address of the bookmark: https://github.com/mchaisso/Ideoplot