BOL: Related items

HiTE: a fast and accurate dynamic boundary adjustment approach for full-length Transposable Elements detection and annotation in Genome Assemblies

LEGE — Sat, 20 Sep 2025 09:34:04 -0500

HiTE is a Python software that uses a dynamic boundary adjustment approach to detect and annotate full-length Transposable Elements in Genome Assemblies. In comparison to other tools, HiTE demonstrates superior performance in detecting a greater number of full-length TEs.

panHiTE

We have developed panHiTE, a comprehensive and accurate pipeline for TE detection in large-scale population genomes. It has been successfully applied to hundreds of plant population genomes, demonstrating its effectiveness and scalability.

For detailed instructions, please refer to the panHiTE tutorial.

Address of the bookmark: https://github.com/CSU-KangHu/HiTE

ClipCrop: a tool for detecting structural variations with single-base resolution using soft-clipping information

Rahul Nayak — Thu, 04 Oct 2018 16:39:28 -0500

This is a tool for detecting structural variations using soft-clipping information From SAM files.

https://github.com/shinout/clipcrop

Address of the bookmark: https://github.com/shinout/clipcrop

Coding Ground

Jitendra Narayan — Tue, 17 Mar 2015 00:47:20 -0500

Online coding group for most of the programming languages.

Code in almost all popular languages using Coding Ground. Edit, compile, execute and share your projects, 100% cloud.

http://www.tutorialspoint.com/codingground.htm

Address of the bookmark: http://www.tutorialspoint.com/codingground.htm

Rosalind Problem Solution with Perl

Jit — Tue, 09 Jun 2015 23:35:18 -0500

Rosalind is a platform for learning bioinformatics and programming through problem solving. Take a tour to get the hang of how Rosalind works.

Bioinformatics Textbook Track

Find more about Rosalind puzzle at http://rosalind.info/problems/list-view/?location=bioinformatics-textbook-track

I will provide solution of all the Rosalind problem with Perl for community.

Check out the right sidebar for more links ...

Bioinformatics Made Easy Search: Bioinformatics tools and run genomic analysis in the cloud

Rahul Nayak — Thu, 20 Aug 2015 02:21:20 -0500

InsideDNA makes hundreds of bioinformatics tools immediately available to run via an easy-to-use web interface and allows an accurate search across all functions, tools and pipelines.

With InsideDNA, you can upload and store your own genomic/genetic datasets in a limitless cloud space, and instantly analyze it with a powerful compute instance, without any tool installation or set up hassle.

More at https://insidedna.me/

Address of the bookmark: https://insidedna.me/

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

Learning Python Programming - a bioinformatician perspective !

Rahul Nayak — Mon, 14 May 2018 16:33:03 -0500

Python Programming is a general purpose programming language that is open source, flexible, powerful and easy to use. One of the most important features of python is its rich set of utilities and libraries for data processing and analytics tasks. In the current era of big biological data, python and biopython is getting more popularity due to its easy-to-use features which supports big data processing.

In this tutorial series article, I will explore features and packages of python which are widely used in the big data, NGS, and bioinformatics. I will also walk through a real biological example which shows NGS data processing with the help of python packages and programming.

Python has a couple of points to recommend it to biologists and scientists specifically:

It's widely used in the scientific community
It has a couple of very well designed libraries for doing complex scientific computing (although we won't encounter them in this book)
It lend itself well to being integrated with other, existing tools
It has features which make it easy to manipulate strings of characters (for example, strings of DNA bases and protein amino acid residues, which we as biologists are particularly fond of)

In general, following are some of the important features of python which makes it a perfect fit for rapid application development.

Python is interpreted language so the program does not need to be compiled. Interpreter parses the program code and generates the output.
Python is dynamically typed, so the variables types are defined automatically.
Python is strongly typed. So the developers need to cast the type manually.
Less code and more use makes it more acceptable.
Python is portable, extendable and scalable.

There are two major Python versions, Python 2 and Python 3. Python 2 and 3 are quite different. This tutorial uses Python 3, because it more semantically correct and supports newer features.

I will post tutorial on daily basis on this page. Check the sub-pages on right side.

ansible: Simple, agentless IT automation that anyone can use

Rahul Nayak — Wed, 17 Apr 2019 21:41:04 -0500

Ansible is a universal language, unraveling the mystery of how work gets done. Turn tough tasks into repeatable playbooks. Roll out enterprise-wide protocols with the push of a button. Give your team the tools to automate, solve, and share.

Address of the bookmark: https://www.ansible.com/

DISCOVAR

Abhimanyu Singh — Mon, 18 Apr 2016 11:59:16 -0500

DISCOVAR is a new variant caller and DISCOVAR de novo a new genome assembler, both designed for state-of-the-art data. Their inputs are chosen to optimize quality while keeping costs low. Currently it takes as input Illumina reads of length 250 or longer — produced on MiSeq or HiSeq 2500 — and from a single PCR-free library. These data enable a level of completeness and continuity that was not previously possible.

DISCOVAR can call variants on a region by region basis, potentially tiling an entire large genome. DISCOVAR variant calling is under active development and transitioning to VCF.

DISCOVAR de novo can generate de novo assemblies for both large and small genomes. It currently does not call variants.

More at https://www.broadinstitute.org/software/discovar/blog/?page_id=14

Address of the bookmark: https://www.broadinstitute.org/software/discovar/blog/

Resolving the complexity of the human genome using single-molecule sequencing

Rahul Agarwal — Sat, 11 Jul 2015 12:47:34 -0500

The human genome is arguably the most complete mammalian reference assembly yet more than 160 euchromatic gaps remain and aspects of its structural variation remain poorly understood ten years after its completion. The results in this paper https://www.genomeweb.com/sequencing/team-uses-single-molecule-sequencing-close-gaps-chart-complexity-human-reference suggest a greater complexity of the human genome in the form of variation of longer and more complex repetitive DNA that can now be largely resolved with the application of this longer-read sequencing technology.

Address of the bookmark: http://www.nature.com/nature/journal/v517/n7536/full/nature13907.html