Genomic Expression and other practical stuff
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Tutorials: NGS assembly, Software, Algorithms

Tutorials

Via Mel Melendrez-Vallard
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Postdoc in Computational Phylogenetics

Ronquist lab, Swedish Museum of Natural History, Stockholm Application deadline: November 30 Our group develops methods for Bayesian phylogenetic and phylogenomic inference.

Via Mel Melendrez-Vallard
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Next-Gen Sequencing Informatics: Advances in analysis and interpretation of NGS Data, Apr 2014

Next-Gen Sequencing Informatics: Advances in analysis and interpretation of NGS Data, Apr 2014 | Genomic Expression and other practical stuff | Scoop.it

Via Mel Melendrez-Vallard
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NEXT-peak: a normal-exponential two-peak model for peak-calling in ChIP-seq data

Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) can locate transcription factor binding sites on genomic scale.

Via Biswapriya Biswavas Misra
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Biswapriya Biswavas Misra's curator insight, May 26, 2013 4:45 AM
Abstract (provisional)Background

Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) can locate transcription factor binding sites on genomic scale. Although many models and programs are available to call peaks, none has dominated its competition in comparison studies.

Results

We propose a rigorous statistical model, the normal-exponential two-peak (NEXT-peak) model, which parallels the physical processes generating the empirical data, and which can naturally incorporate mappability information. The model therefore estimates total strength of binding (even if some binding locations do not map uniquely into a reference genome, effectively censoring them); it also assigns an error to an estimated binding location. The comparison study with existing programs on real ChIP-seq datasets (STAT1, NRSF, and ZNF143) demonstrates that the NEXT-peak model performs well both in calling peaks and locating them. The model also provides a goodness-of-fit test, to screen out spurious peaks and to infer multiple binding events in a region.

Conclusions

The NEXT-peak program calls peaks on any test dataset about as accurately as any other, but provides unusual accuracy in the estimated location of the peaks it calls. NEXT-peak is based on rigorous statistics, so its model also provides a principled foundation for a more elaborate statistical analysis of ChIP-seq data.

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RNA-Seq Atlas—a reference database for gene expression profiling in normal tissue by next-generation sequencing

RNA-Seq Atlas—a reference database for gene expression profiling in normal tissue by next-generation sequencing | Genomic Expression and other practical stuff | Scoop.it
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Insights into organ-specific pathogen defense responses in plants: RNA-seq ... - 7thSpace Interactive (press release)

Insights into organ-specific pathogen defense responses in plants: RNA-seq ...
7thSpace Interactive (press release)
A total of 483 million paired end Illumina RNA-seq reads were generated, representing the transcription of around 30,000 potato genes.
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A small comparison of bio-equivalence calculations.

A small comparison of bio-equivalence calculations. | Genomic Expression and other practical stuff | Scoop.it
(This article was first published on Wiekvoet, and kindly contributed to R-bloggers)
Last week I looked at two-way cross-over studies and followed the example of Schütz (http://bebac.at/) in the analysis.
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So you want to be a computational biologist? : Nature Biotechnology : Nature Publishing Group

Two computational biologists give advice when starting out on computational projects.

Via Pedro Fernandes, Ali Taheri
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Pedro Fernandes's curator insight, November 10, 2013 2:21 PM

Well done @pathogenomenick and  @biomickwatson

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RNA-seq Showdown | RNA-Seq Blog

by Kelly Rae Chi - Biotechniques Five RNA-seq library preparation methods go head-to-head in terms of performance in low-quality and low quantity RNA (#RNAseq library prep showdown.
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The Guerilla Guide to R

The Guerilla Guide to R | Genomic Expression and other practical stuff | Scoop.it
Update: I am aware the table of contents are being displayed in bullet form as I intended. The web template I'm using seems to be buggy. It also seems to think this page is in Indonesian...Working on it!
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TA-AFADM13 Transcriptome Assembly, Automatic Annotation and Data Mining

TA-AFADM13 Transcriptome Assembly, Automatic Annotation and Data Mining | Genomic Expression and other practical stuff | Scoop.it

This is a combined course in RNA-seq and functional annotation aimed at scientists working in next generation sequencing transcriptomics who want to extract the best possible information from their data.

The first part of the course is a practical tutorial on Blast2GO, the most popular framework for functional annotation of sequences. Participants will also learn how to generate de novo functional labels, such as GO terms and KEGG pathways, for their sequence data and how to extract relevant information from these annotations, i.e. visualization and enrichment analysis using the Gene Ontology. The second part of the course covers many relevant aspects of RNA-seq analysis, form quality control, to mapping, reconstruction of transcripts and differential expression. Participants will learn how to deal with RNAseq data both in the presence and absence of a reference genome.

At the end of the course, participants can expect to have acquired skills to use multiple software tools to analise RNA-seq data and use Blast2GO.

The course will be based on the use of Blast2GO application and will mainly comprise exercises and practical cases. Participants may bring their own data.

Who should attend? Experimentalists and bioinformaticians working on EST, NextGeneration Sequencing and microarray design projects, specially (but not exclusively) of non-model species. No programming knowledge is required.

 

Course Pre-requisites:

Basic knowledge in Molecular Biology and Statistics.

 


Via Pedro Fernandes
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Pedro Fernandes's curator insight, April 8, 2013 7:04 PM

with Ana Conesa and Fernando Garcia-Alcalde