BOL: Related items

alluvial diagram

Jit — Sat, 21 Sep 2019 07:31:08 -0500

Alluvial diagram is a variant of a Parallel Coordinates Plot (PCP) but for categorical variables. Variables are assigned to vertical axes that are parallel. Values are represented with blocks on each axis. Observations are represented with alluvia (sing. “alluvium”) spanning across all the axes.

Address of the bookmark: https://cran.r-project.org/web/packages/alluvial/vignettes/alluvial.html

EFS: an ensemble feature selection tool implemented as R-package and web-application

Jit — Tue, 28 Jan 2020 05:12:23 -0600

The software EFS (Ensemble Feature Selection) makes use of multiple feature selection methods and combines their normalized outputs to a quantitative ensemble importance. Currently, eight different feature selection methods have been integrated in EFS, which can be used separately or combined in an ensemble.

https://biodatamining.biomedcentral.com/articles/10.1186/s13040-017-0142-8

Address of the bookmark: http://efs.heiderlab.de/

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

EMBO Practical Course, Bioinformatics, large-scale data, at Shenzhen, China

Wed, 14 Aug 2013 09:50:56 -0500

This international advanced course will provide training on bioinformatics and statistics methods for genomic research. It will give insight into how biological knowledge can be generated from high-throughput sequencing (DNA-Seq, RNA-seq, ChIP-seq) experiments and will illustrate how to analyze such data. The course covers both the underlying statistical and algorithmic concepts, and the practice of how to automate and code such analyses using the scripting language R.

17 Nov 2013 -22 Nov 2013

More at http://events.embo.org/13-large-scale-data/

Online Registration: https://www.conference-service.com/pc13-47/welcome.cgi

LRCstats: Long Read Correction Statistics

Jit — Fri, 05 Jan 2018 04:04:20 -0600

LRCstats is an open-source pipeline for benchmarking DNA long read correction algorithms for long reads outputted by third generation sequencing technology such as machines produced by Pacific Biosciences. The reads produced by third generation sequencing technology, as the name suggests, are longer in length than reads produced by next generation sequencing technologies, such as those produced by Illumina. However, long reads are plagued by high error rates, which can cause issues in downstream analysis. Long read correction algorithms reduce the error rate of long reads either through self-correcting methods or using accurate, short reads outputted by next generation sequencing technologies to correct long reads.

Of course, some long read correction algorithms are better than others, and developers of long read correction algorithms will wish to compare their algorithm with others currently available. LRCstats benchmarks long read correction algorithms using long reads produced by simulators (such as SimLoRD or PBSim) where the two-way alignments between the uncorrected long reads (uLR) and the corresponding sequences in the reference genome (Ref) are given in some sort of alignment file and then aligning the corrected long reads (cLR) to the Ref-uLR two-way alignments to create three-way alignments using a dynamic programming algorithm. Statistics on these three-way alignments are then collected, such as the overall error rates of the corrected long reads.

https://www.healthcare.uiowa.edu/labs/au/LSC/

Address of the bookmark: https://github.com/cchauve/lrcstats

AMStat: display statistics of large sequence files from next generation sequencing projects

Neel — Fri, 09 Nov 2018 13:34:56 -0600

SAMStat is an efficient C program to quickly display statistics of large sequence files from next generation sequencing projects. When applied to SAM/BAM files all statistics are reported for unmapped, poorly and accurately mapped reads separately. This allows for identification of a variety of problems, such as remaining linker and adaptor sequences, causing poor mapping. Apart from this SAMStat can be used to verify individual processing steps in large analysis pipelines.

Address of the bookmark: http://samstat.sourceforge.net/

Statistical for biological research

Neel — Thu, 03 Nov 2016 04:59:48 -0500

There is no disputing the importance of statistical analysis in biological research, but too often it is considered only after an experiment is completed, when it may be too late.

This collection highlights important statistical issues that biologists should be aware of and provides practical advice to help them improve the rigor of their work.

Nature Methods' Points of Significance column on statistics explains many key statistical and experimental design concepts. Other resources include an online plotting tool and links to statistics guides from other publishers.

Address of the bookmark: http://www.nature.com/collections/qghhqm