BOL: Related items

AfterQC: Automatic Filtering, Trimming, Error Removing and Quality Control for fastq data

Jit — Fri, 29 Jun 2018 03:26:03 -0500

Automatic Filtering, Trimming, Error Removing and Quality Control for fastq data AfterQC can simply go through all fastq files in a folder and then output three folders: good, bad and QC folders, which contains good reads, bad reads and the QC results of each fastq file/pair. Currently it supports processing data from HiSeq 2000/2500/3000/4000, Nextseq 500/550, MiniSeq...and other Illumina 1.8 or newer formats The author has reimplemented this tool in C++ with multithreading support to make it much faster. The new tool is called fastp and can be found at: https://github.com/OpenGene/fastp . If you prefer a C++ based tool, please use fastp instead. https://github.com/OpenGene/AfterQC

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

MITObim - mitochondrial baiting and iterative mapping

Rahul Nayak — Tue, 08 May 2018 04:15:25 -0500

This document contains instructions on how to use the MITObim pipeline described in Hahn et al. 2013. The full article can be found here. Kindly cite the article if you are using MITObim in your work. The pipeline was originally developed for Illumina data, but thanks to the versatility of the MIRA assembler, MITObim supports in principle also data from the Iontorrent, 454 and PacBio sequencing platforms.

Below you can find a few basic tutorials for how to run MITObim and I encorage you to give them a try with the testdata that comes with this Repo, just to make sure everything is running smoothly on your system. It'll only take a few minutes and will potentially safe you a lot of time down the line.

I provide further examples here as Jupyter notebooks. Get in touch if you feel like sharing your particular MITObim solution and I'd be happy to put it up here, too!

Address of the bookmark: https://github.com/chrishah/MITObim

Pimp your brain: Bioinformatics

Wed, 20 Aug 2014 22:09:21 -0500

Jan Lisec from the Max Planck Institute of Molecular Plant Physiology explains, in this "pimp your brain" episode, what bioinformatics is and why bioinformatics is so important and indispensable for biological research. In the video serial "Pimp your brain" scientists from the Max Planck Institute of Molecular Plant Physiology describe their research. More videos from the 'Pimp your brain' serial are available on www.youtube.com/playlist?list=PL-l9VItC9Gn2Ur2Xj6PTOAkjLUlVPbIOO More videos are available on www.mpimp-golm.mpg.de

A guide to machine learning for biologists

Abhi — Tue, 28 Dec 2021 01:43:25 -0600

Because of the increasing size and inherent complexity of biological data, there has been an increase in the application of machine learning in biology to create useful and predictive models of the underlying biological processes. All machine learning techniques fit models to data; nevertheless, the specific methods are highly variable and can appear baffling at first glance. In this Review, we hope to give readers a moderate introduction to a few fundamental machine learning techniques, including the most recently created and frequently used deep neural network techniques. We illustrate how different algorithms may be adapted to specific types of biological data, as well as some best practises and points to consider when embarking on machine learning studies. There is also discussion of several upcoming directions in machine learning methodology.

Address of the bookmark: https://www.nature.com/articles/s41580-021-00407-0

Machine learning training and courses in bioinformatics !

Rahul Nayak — Tue, 31 Dec 2019 19:33:07 -0600

Machine learning techniques have been successful in analyzing biological data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. In this class, we will learn basics about probabilistic models and machine learning techniques. We will focus on probabilistic models (Markov models, Hidden Markov models, and Bayesian networks) for biological sequence analysis and systems biology. Other machine learning techniques, such as Naive bayes, neural networks and SVMs will only be covered briefly.

More at http://homes.sice.indiana.edu/yye/lab/teaching/spring2017-I529/

Ten quick tips for deep learning in biology

Neel — Fri, 25 Mar 2022 18:35:12 -0500

By taking a comprehensive and careful approach to deep learning based on critical thinking about research questions, planning to maintain rigor, and discerning how work might have far-reaching consequences with ethical dimensions, the life science community can advance reproducible, interpretable, and high-quality science that is enriching and beneficial for both scientists and society.

Address of the bookmark: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009803

DBG2OLC:Efficient Assembly of Large Genomes Using Long Erroneous Reads of the Third Generation Sequencing Technologies

Jit — Wed, 19 Apr 2017 10:09:51 -0500

DBG2OLC:Efficient Assembly of Large Genomes Using Long Erroneous Reads of the Third Generation Sequencing Technologies

Our work is published in Scientific Reports:

Ye, C. et al. DBG2OLC: Efficient Assembly of Large Genomes Using Long Erroneous Reads of the Third Generation Sequencing Technologies. Sci. Rep. 6, 31900; doi: 10.1038/srep31900 (2016).

http://www.nature.com/articles/srep31900

The manual can be downloaded from:

https://github.com/yechengxi/DBG2OLC/raw/master/Manual.docx

To use precompiled versions,please go to:

https://github.com/yechengxi/DBG2OLC/tree/master/compiled

Address of the bookmark: https://github.com/yechengxi/DBG2OLC

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

Reference-free prediction of rearrangement breakpoint reads

Neel — Thu, 08 Mar 2018 05:05:25 -0600

lideSort-BPR ( b reak p oint r eads) is based on a fast algorithm for all-against-all comparisons of short reads and theoretical analyses of the number of neighboring reads. When applied to a dataset with a sequencing depth of 100×, it finds ∼88% of the breakpoints correctly with no false-positive reads. Moreover, evaluation on a real prostate cancer dataset shows that the proposed method predicts more fusion transcripts correctly than previous approaches, and yet produces fewer false-positive reads. To our knowledge, this is the first method to detect breakpoint reads without using a reference genome.

https://github.com/ewijaya/slidesort-bpr

Address of the bookmark: https://code.google.com/archive/p/slidesort-bpr/

TAREAN: A computational tool for identification and characterization of satellite DNA from unassembled short reads

Surabhi Chaudhary — Tue, 15 May 2018 02:53:11 -0500

TAndem REpeat ANalyzer -TAREAN – is a computational pipeline for unsupervised identification of satellite repeats from unassembled sequence reads. The pipeline uses low-pass whole genome sequence reads and performs their graph-based clustering. Resulting clusters, representing all types of repeats, are then examined for the presence of circular structures and putative satellite repeats are reported.

How to use TAREAN:

Install a local instance of the pipeline using its source code available from bitbucket repository.
Use public Galaxy-based server at https://repeatexplorer-elixir.cerit-sc.cz/. The server is provided in frame of the Elixir CZ project and is maintained by CESNET and CERIT-SC. Simple registration is required to use this service.

Development of TAREAN was supported by ELIXIR CZ research infrastructure project (MEYS Grant No: LM2015047).

References

Novak, P., Avila Robledillo, L., Koblizkova, A., Vrbova, I., Neumann, P., Macas, J. (2017) – TAREAN: a computational tool for identification and characterization of satellite DNA from unassembled short reads. Nucleic Acids Res., doi:10.1093/nar/gkx257

Address of the bookmark: https://bitbucket.org/petrnovak/repex_tarean