<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/41804?offset=30 https://bioinformaticsonline.com/bookmarks/view/38692/geneck-gene-network-construction-kit-is-a-comprehensive-online-tool-kit-that-integrate-various-statistical-methods-to-construct-gene-networks Tue, 15 Jan 2019 09:39:30 -0600 https://bioinformaticsonline.com/bookmarks/view/38692/geneck-gene-network-construction-kit-is-a-comprehensive-online-tool-kit-that-integrate-various-statistical-methods-to-construct-gene-networks <![CDATA[GeNeCK (Gene Network Construction Kit) is a comprehensive online tool kit that integrate various statistical methods to construct gene networks]]> GeNeCK (Gene Network Construction Kit) is a comprehensive online tool kit that integrate various statistical methods to construct gene networks based on gene expression data and optional hub gene information.

It efficiently constructs gene networks from expression data. It allows the user to use ten different network construction methods (such as partial correlation-, likelihood-, Bayesian- and mutual information-based methods) and integrates the resulting networks from multiple methods. Hub gene information, if available, can be incorporated to enhance performance.

GeNeCK is an efficient and easy-to-use web application for gene regulatory network construction. It can be accessed at http://lce.biohpc.swmed.edu/geneck

Address of the bookmark: http://lce.biohpc.swmed.edu/geneck/

]]> Rahul Nayak https://bioinformaticsonline.com/bookmarks/view/44497/graphpath-a-graph-attention-model-for-molecular-stratification-with-interpretability-based-on-the-pathway-pathway-interaction-network Wed, 27 Mar 2024 20:51:21 -0500 https://bioinformaticsonline.com/bookmarks/view/44497/graphpath-a-graph-attention-model-for-molecular-stratification-with-interpretability-based-on-the-pathway-pathway-interaction-network <![CDATA[GraphPath: A graph attention model for molecular stratification with interpretability based on the pathway-pathway interaction network]]> Achieving accurate and interpretable clinical predictions requires paramount attention to thoroughly characterizing patients at both the molecular and biological pathway levels. In this paper, we present GraphPath, a biological knowledge-driven graph neural network with multi-head self-attention mechanism that implements the pathway-pathway interaction network. We train GraphPath to classify the cancer status of patients with prostate cancer based on their multi-omics profiling.

image

Address of the bookmark: https://github.com/amazingma/GraphPath

]]> LEGE https://bioinformaticsonline.com/bookmarks/view/42499/galaxy-training-resources Sun, 27 Dec 2020 05:28:07 -0600 https://bioinformaticsonline.com/bookmarks/view/42499/galaxy-training-resources <![CDATA[Galaxy Training Resources !]]> Welcome to Galaxy Training!

Collection of tutorials developed and maintained by the worldwide Galaxy community

TopicTutorials
Introduction to Galaxy Analyses 10
Assembly 6
Climate 3
Computational chemistry 6
Ecology 6
Epigenetics 6
Genome Annotation 3
Imaging 3
Metabolomics 4
Metagenomics 7
Proteomics 18
Sequence analysis 2
Statistics and machine learning 8
Transcriptomics 23
Variant Analysis 8
Visualisation 2

 

Address of the bookmark: https://training.galaxyproject.org/training-material/

]]> Jit https://bioinformaticsonline.com/blog/view/44002/interesting-bioinformatics-resources Fri, 11 Nov 2022 06:30:46 -0600 https://bioinformaticsonline.com/blog/view/44002/interesting-bioinformatics-resources <![CDATA[Interesting Bioinformatics Resources !]]> 1. a reproducible workflow. https://www.youtube.com/watch?v=s3JldKoA0zw This two minute video will change your mind on reproducible research 

2. Parallel sequencing lives, or what makes large sequencing projects successful https://academic.oup.com/gigascience/article/6/11/gix100/4557140?login=false

3. Common-sense approaches to sharing tabular data alongside publication https://www.sciencedirect.com/science/article/pii/S2666389921002300

4. A Reproducible Data Analysis Workflow with R Markdown, Git, Make, and Docker https://psyarxiv.com/8xzqy/

5. Practical Computational Reproducibility in the Life Sciences https://www.cell.com/cell-systems/fulltext/S2405-4712(18)30140-6

6. A video by Dr.Keith A. Baggerly from MD Anderson [The Importance of Reproducible Research in High-Throughput Biology](https://www.youtube.com/watch?v=7gYIs7uYbMo) highly recommended.

7. Ten Simple Rules for Reproducible Computational Research http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003285)

8. Good Enough Practices in Scientific Computing http://arxiv.org/abs/1609.00037 

9. Best Practices for Scientific Computing https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1001745

10. A Quick Guide to Organizing Computational Biology Projects http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.100042  A must read for computational biologists!

11. Reproducibility of computational workflows is automated using continuous analysis https://www.nature.com/articles/nbt.3780

12. Five selfish reasons to work reproducibly https://genomebiology.biomedcentral.com/articles/10.1186/s13059-015-0850-7

]]> Abhi https://bioinformaticsonline.com/pages/view/35805/python-learning-resources-for-bioinformatics-and-computational-biologist Fri, 02 Mar 2018 06:54:15 -0600 https://bioinformaticsonline.com/pages/view/35805/python-learning-resources-for-bioinformatics-and-computational-biologist <![CDATA[Python learning resources for bioinformatics and computational biologist !]]> Python is a general-purpose language, which means it can be used to build just about anything, which will be made easy with the right tools/libraries.

Professionally, Python is great for backend web development, data analysis, artificial intelligence, and scientific computing. Many developers have also used Python to build productivity tools, games, and desktop apps, so there are plenty of resources to help you learn how to do those as well.

For pros and cons visit http://www.bestprogramminglanguagefor.me/why-learn-python and http://bioinformaticsonline.com/discussion/view/459/python-vs-perl

More resources at https://github.com/CodementorIO/Python-Learning-Resources

Following are the list of useful python programming resources:

]]> Jit https://bioinformaticsonline.com/bookmarks/view/44601/free-resources-to-learn-statistics Sat, 06 Jul 2024 10:30:50 -0500 https://bioinformaticsonline.com/bookmarks/view/44601/free-resources-to-learn-statistics <![CDATA[Free resources to learn statistics]]> Welcome to the course notes for STAT 414: Introduction to Probability Theory. These notes are designed and developed by Penn State's Department of Statistics and offered as open educational resources. These notes are free to use under Creative Commons license CC BY-NC 4.0.

 

A free online version of the second edition of the book based on Stat 110, Introduction to Probability by Joe Blitzstein and Jessica Hwang, is now available at http://probabilitybook.net

Print copies are available via CRC Press, Amazon, and elsewhere. 

Stat110x is also available as an edX course. Free signup at https://www.edx.org/course/introduction-to-probability-0

The edX course focuses on animations, interactive features, readings, and problem-solving, and is complementary to the Stat 110 lecture videos on YouTube, which are available at https://goo.gl/i7njSb

The Stat110x animations are available within the course and at https://goo.gl/g7pqTo

https://projects.iq.harvard.edu/stat110/home 

Address of the bookmark: https://online.stat.psu.edu/stat414/

]]> BioStar https://bioinformaticsonline.com/bookmarks/view/34292/automatic-filtering-trimming-error-removing-and-quality-control-for-fastq-data Mon, 13 Nov 2017 05:10:23 -0600 https://bioinformaticsonline.com/bookmarks/view/34292/automatic-filtering-trimming-error-removing-and-quality-control-for-fastq-data <![CDATA[Automatic Filtering, Trimming, Error Removing and Quality Control for fastq data]]> 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

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

]]> Rahul Nayak https://bioinformaticsonline.com/bookmarks/view/34504/minion-gc-an-r-script-to-do-some-qc-on-minion-data Sun, 03 Dec 2017 15:19:18 -0600 https://bioinformaticsonline.com/bookmarks/view/34504/minion-gc-an-r-script-to-do-some-qc-on-minion-data <![CDATA[MinION_GC: An R script to do some QC on MinION data]]> Other tools focus on getting data out of the fastq or fast5 files, which is slow and computationally intensive. The benefit of this approach is that it works on a single, small, .txt summary file. So it's a lot quicker than most other things out there: it takes about a minute to analyse a 4GB flowcell on my laptop.

https://github.com/roblanf/minion_qc

Address of the bookmark: https://github.com/roblanf/minion_qc

]]> Radha Agarkar https://bioinformaticsonline.com/bookmarks/view/36518/mix-combining-multiple-assemblies-from-ngs-data Tue, 08 May 2018 04:58:05 -0500 https://bioinformaticsonline.com/bookmarks/view/36518/mix-combining-multiple-assemblies-from-ngs-data <![CDATA[MIX: Combining multiple assemblies from NGS data]]> Mix is a tool that combines two or more draft assemblies, without relying on a reference genome and has the goal to reduce contig fragmentation and thus speed-up genome finishing. The proposed algorithm builds an extension graph where vertices represent extremities of contigs and edges represent existing alignments between these extremities. These alignment edges are used for contig extension. The resulting output assembly corresponds to a path in the extension graph that maximizes the cumulative contig length.

The Mix algorithm, approach and results were published in BMC bioinformatics : http://www.biomedcentral.com/1471-2105/14/S15/S16.

Address of the bookmark: https://github.com/cbib/MIX

]]> Rahul Nayak https://bioinformaticsonline.com/bookmarks/view/37498/nextsv-a-meta-caller-for-structural-variants-from-low-coverage-long-read-sequencing-data Mon, 06 Aug 2018 17:24:53 -0500 https://bioinformaticsonline.com/bookmarks/view/37498/nextsv-a-meta-caller-for-structural-variants-from-low-coverage-long-read-sequencing-data <![CDATA[NextSV: a meta-caller for structural variants from low-coverage long-read sequencing data]]> NextSV, a meta SV caller and a computational pipeline to perform SV calling from low coverage long-read sequencing data. NextSV integrates three aligners and three SV callers and generates two integrated call sets (sensitive/stringent) for different analysis purpose. The output of NextSV is in ANNOVAR-compatible bed format. Users can easily perform downstream annotation using ANNOVAR and disease gene discovery using Phenolyzer.

 

 

Address of the bookmark: https://github.com/Nextomics/NextSV

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