BOL: All site bookmarks

LncPipe:A Nextflow-based pipeline for comprehensive analyses of long non-coding RNAs from RNA-seq datasets

LEGE — Fri, 17 Sep 2021 01:57:02 -0500

The pipeline was developed based on a popular workflow framework Nextflow, composed of four core procedures including reads alignment, assembly, identification and quantification. It contains various unique features such as well-designed lncRNAs annotation strategy, optimized calculating efficiency, diversified classification and interactive analysis report. LncPipe allows users additional control in interuppting the pipeline, resetting parameters from command line, modifying main script directly and resume analysis from previous checkpoint.

Ref https://www.lncrnablog.com/lncpipe-a-nextflow-based-pipeline-for-identification-and-analysis-of-long-non-coding-rnas-from-rna-seq-data/

Address of the bookmark: https://github.com/likelet/LncPipe

HISAT2 Index Files Download !

LEGE — Wed, 15 Sep 2021 22:17:49 -0500

Resource for downloading all the HISAT2 related files

Please cite:

Kim, D., Paggi, J.M., Park, C. et al. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat Biotechnol 37, 907–915 (2019). https://doi.org/10.1038/s41587-019-0201-4

Address of the bookmark: http://daehwankimlab.github.io/hisat2/download/#h-sapiens

Reference Sequence Resource!

LEGE — Wed, 15 Sep 2021 21:15:22 -0500

The ENCODE project uses Reference Genomes from NCBI or UCSC to provide a consistent framework for mapping high-throughput sequencing data. In general, ENCODE data are mapped consistently to 2 human (GRCH38, hg19) and 2 mouse (mm9/mm10) genomes for historical comparability. Drosophia melanogaster experiments are mapped to either dm3 or dm6 and Caenorhabdilis elegans experiments are mapped to ce10 or ce11. T

Address of the bookmark: https://www.encodeproject.org/data-standards/reference-sequences/

A guide to machine learning for biologists

LEGE — Wed, 15 Sep 2021 13:21:08 -0500

We aim to provide readers with a gentle introduction to a few key machine learning techniques, including the most recently developed and widely used techniques involving deep neural networks. We describe how different techniques may be suited to specific types of biological data, and also discuss some best practices and points to consider when one is embarking on experiments involving machine learning. Some emerging directions in machine learning methodology are also discussed.

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

RagTag: a collection of software tools for scaffolding and improving modern genome assemblies

Jit — Sat, 11 Sep 2021 00:28:14 -0500

RagTag is a collection of software tools for scaffolding and improving modern genome assemblies. Tasks include:

Homology-based misassembly correction
Homology-based assembly scaffolding and patching
Scaffold merging

Address of the bookmark: https://github.com/malonge/RagTag

Machine Learning for Genomics

Jit — Thu, 09 Sep 2021 11:26:32 -0500

Module 1: Statistics for genomics (2-8 August 2021)

A simple intro to statistical distributions
hypothesis testing
linear models.

reading: http://compgenomr.github.io/book/stats.html

slides: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week1/compgen2021_stats.pdf

exercises+code: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week1/

Module 2: Unsupervised learning for genomics (9-15 August 2021)

Understanding basic intuition behind machine learning approaches.
Using unsupervised learning to cluster and visualise data points
Dimension reduction techniques for visualisation and as input to clustering methods

reading: http://compgenomr.github.io/book/unsupervisedLearning.html

slides: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week2/compgen2021_unsupervisedLearning.pdf

exercises+code: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week2/

Module 3: Supervised learning for genomics (16-22 August 2021)

Understanding and using supervised learning methods for predictive purposes
How to measure prediction performance
Understand and use cross-validation and related concepts

reading: http://compgenomr.github.io/book/supervisedLearning.html

slides: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week3/compgen2021_supervisedLearning.pdf

exercises+code: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week3/

https://github.com/BIMSBbioinfo/compgen2021

Address of the bookmark: https://github.com/BIMSBbioinfo/compgen2021

JUDI: Just Do It

Jit — Mon, 06 Sep 2021 02:44:35 -0500

judi comes from the idea of bringing the power and efficiency of doit to execute any kind of task under many combinations of parameter settings.

https://github.com/ncbi/JUDI

Address of the bookmark: https://github.com/ncbi/JUDI

biostarhandbook

Neel — Fri, 27 Aug 2021 01:31:01 -0500

Nice book collection for bioinformatician ... highly recommended.

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

Applying MinHash to cluster RNAseq samples

Neel — Thu, 26 Aug 2021 10:37:19 -0500

A kmer blog worth spending time on

Address of the bookmark: http://ivory.idyll.org/blog/2016-sourmash.html

k-mers tutorial - classification and taxonomy

Neel — Thu, 26 Aug 2021 10:28:43 -0500

DNA k-mers underlie much of our assembly work, and we (along with many others!) have spent a lot of time thinking about how to store k-mer graphs efficiently, discard redundant data, and count them efficiently.

More recently, we've been enthused about using k-mer based similarity measures and computing and searching k-mer-based sketch search databases for all the things.

But I haven't spent too much talking about using k-mers for taxonomy, although that has become an ahem area of interest recently, if you read into our papers a bit.

In this blog post I'm going to fix this by doing a little bit of a literature review and waxing enthusiastic about other people's work. Then in a future blog post I'll talk about how we're building off of this work in fun! and interesting? ways!

Address of the bookmark: http://ivory.idyll.org/blog/2017-something-about-kmers.html