BOL: Related items

Postdoctoral scientist genome analytics/ genome bioinformatics (m/f/*)

Sun, 16 Feb 2020 02:57:40 -0600

https://www.uksh.de/jobs/Stellenangebote-nr-20190570-p-8.html
Your profile:
Degree in bioinformatics, biostatistics, or equivalent
Experience in the processing and analysis of large-scale genomics data using compute clusters / high-performance computing
Strong competence in working in Unix/Linux environments (shell)
Strong programming skills (in particular: Python, R, Perl)
Experience with using git and snakemake
Fluent English language skills, both spoken and written
Strong communication skills and motivation to work in a young, interdisciplinary, dynamic team

Additional Information:

If you have any questions about scientific aspects of this position, please contact Prof. Lars Bertram, head of LIGA (lars.bertram@uni-luebeck.de).

Please contact Ms. Anna Wolbert for further questions about administrative details (recruiting@uksh.de).

Weitere Informationen erhalten Sie auch unter www.uksh.de/karriere.

Wir freuen uns auf Ihre Bewerbung bis zum 15.03.2020 unter Angabe unserer Ausschreibungsnummer 20190570.119.CL.

New Machine Learning Packages in R

Rahul Nayak — Fri, 27 Mar 2020 12:11:21 -0500

Machine Learning

autokeras v1.0.1: Implements an interface to AutoKeras, an open source software library for automated machine learning. See README for an example.

MTPS v0.1.9: Implements functions to predict simultaneous multiple outcomes based on revised stacking algorithms as described in Xing et al. (2019). See the vignette to get started.

quanteda.textmodels v0.9.1: Implements methods for scaling models and classifiers based on sparse matrix objects representing textual data. It includes implementations of the Laver et al. (2003) wordscores model, the Perry & Benoit’s (2017) class affinity scaling model, and the Slapin & Proksch (2008) wordfish model. See the vignette to get started.

SeqDetect v1.0.7: Implements the automaton model found in Krleža, Vrdoljak & Brčić (2019) to detect and process sequences. See the vignette for examples and theory.

studyStrap v1.0.0: Implements multi-Study Learning algorithms such as Merging, Study-Specific Ensembling (Trained-on-Observed-Studies Ensemble), the Study Strap, and the Covariate-Matched Study Strap. and offers over 20 similarity measures. See Kishida, et al. (2019) for background and the vignette for how to use the package.

QuasR: Quantification and annotation of short reads in R

Neel — Fri, 13 Aug 2021 07:44:05 -0500

The QuasR package (short for Quantify and annotate short reads in R) integrates the functionality of several R packages (such as IRanges (Lawrence et al. 2013) and Rsamtools) and external software (e.g. bowtie, through the Rbowtie package, and HISAT2, through the Rhisat2 package). The package aims to cover the whole analysis workflow of typical high throughput sequencing experiments, starting from the raw sequence reads, over pre-processing and alignment, up to quantification. A single R script can contain all steps of a complete analysis, making it simple to document, reproduce or share the workflow containing all relevant details.

Address of the bookmark: https://www.bioconductor.org/packages/devel/bioc/vignettes/QuasR/inst/doc/QuasR.html

ReConPlot: an R package for the visualization and interpretation of genomic rearrangements

LEGE — Thu, 14 Dec 2023 12:33:19 -0600

ReConPlot (REarrangement and COpy Number PLOT), an R package that provides functionalities for the joint visualization of SCNAs and SVs across one or multiple chromosomes. ReConPlot is based on the popular ggplot2 package, thus allowing customization of plots and the generation of publication-quality figures with minimal effort.

Address of the bookmark: https://academic.oup.com/bioinformatics/article/39/12/btad719/7460198?login=false

Finding Patterns in Biological Sequences

Jit — Thu, 22 Dec 2016 10:30:49 -0600

In this report we provide an overview of known techniques for discovery of patterns of biological sequences (DNA and proteins). We also provide biological motivation, and methods of biological verification of such patterns. Finally we list publicly available tools and databases for pattern discovery. On-line supplement is available through http://genetics.uwaterloo.ca/∼tvinar/cs798g/motif.

Address of the bookmark: http://engr.case.edu/li_jing/papers/00798gpattern.pdf

genomenotebook

Abhi — Thu, 20 Apr 2023 13:19:01 -0500

https://dbikard.github.io/genomenotebook/

Install

pip install genomenotebook

How to use

Create a simple genome browser with a search bar. The sequence appears when zooming in.

import genomenotebook as gn

g=gn.GenomeBrowser(genome_path, gff_path, init_pos=10000)
g.show()

Tracks can be added to visualize your favorite genomics data. See Examples for more !!!!

Address of the bookmark: https://dbikard.github.io/genomenotebook/

VAGUE:Velvet Assembler Graphical Front End

Jit — Fri, 24 Feb 2017 08:56:49 -0600

VAGUE is a vague acronym for "Velvet Assembler Graphical Front End", which means it is a GUI for the Velvet de novo assembler. The command line version of Velvet can be complicated for beginners to use, but VAGUE makes it clear and simple

More at http://www.vicbioinformatics.com/software.vague.shtml

Address of the bookmark: http://www.vicbioinformatics.com/software.vague.shtml

Murasaki

Anjana — Fri, 30 Sep 2016 10:22:30 -0500

Murasaki is an anchor alignment program that is

exteremely fast (17 CPU hours for whole Human x Mouse genome (with 40 nodes: 35 wall minutes), or 8 mammals in 21 CPU hours (42 wall minutes))
scalable (Arbitrarily parallelizable across multiple nodes using MPI)
memory efficient. (Even a single node with 16GB of ram can handle over 1Gbp of sequence)
unlimited by pattern length or selection
repeat tolerant

Address of the bookmark: http://murasaki.dna.bio.keio.ac.jp/wiki/index.php?Murasaki

MafTools

Jit — Thu, 16 Feb 2017 11:16:01 -0600

maftools - An R package to summarize, analyze and visualize MAF files. Introduction.

With advances in Cancer Genomics, Mutation Annotation Format (MAF) is being widley accepted and used to store variants detected. The Cancer Genome Atlas Project has seqenced over 30 different cancers with sample size of each cancer type being over 200. The resulting data consisting of genetic variants is stored in the form of Mutation Annotation Format. This package attempts to summarize, analyze, annotate and visualize MAF files in an efficient manner either from TCGA sources or any in-house studies as long as the data is in MAF format. Maftools can also handle ICGC Simple Somatic Mutation format.

maftools is on bioRxiv

Please cite the below if you find this tool useful for you.

Mayakonda, A. and H.P. Koeffler, Maftools: Efficient analysis, visualization and summarization of MAF files from large-scale cohort based cancer studies. bioRxiv, 2016. doi: http://dx.doi.org/10.1101/052662

Address of the bookmark: https://github.com/PoisonAlien/maftools

PhenoGram

Jit — Tue, 07 Mar 2017 08:35:12 -0600

With PhenoGram researchers can create chomosomal ideograms annotated with lines in color at specific base-pair locations, or colored base-pair to base-pair regions, with or without other annotation. PhenoGram allows for annotation of chromosomal locations and/or regions with shapes in different colors, gene identifiers, or other text. PhenoGram also allows for creation of plots showing expanded chromosomal locations, providing a way to show results for specific chromosomal regions in greater detail.

Address of the bookmark: http://ritchielab.psu.edu/software/phenogram-downloads