In order to promote interaction between the local bioinformatics community and share information on the latest bioinformatics research, the unit organizes a regular Bioinformatics Journal Club.
Coming sessions (always at 10.15):
- May 24th (BioCity B, 5th floor, large seminar room)
A machine learning approach for the identification of key markers involved in brain development from single-cell transcriptomic data by Yongli Hu, Takeshi Hase, Hui Peng Li, Shyam Prabhakar, Hiroaki Kitano, See Kiong Ng, Samik Ghosh and Lawrence Jin Kiat Wee.
How the Club works:
The aim of the club is to enhance interaction between people working with bioinformatics at CBT and other university departments and share information on important topics of the field.
Please feel free to contribute and present an article of interest. The article can be any relatively recent report related to data analysis you think may be interesting to others working on ngs data analysis as well. Prepare to shortly present the article in the beginning of the session (<20min) after which it and the thoughts it provokes will be discussed between the participants.
Contact asta.laiho (_at_) utu.fi or mirkka.ruotsalainen (_at_) utu.fi for the details.
Earlier presented in the club:
- 26.4.2017:A comprehensive and scalable database search system for metaproteomics by Sandip Chatterjee, Gregory S. Stupp, Sung Kyu Robin Park, Jean-Christophe Ducom, John R. Yates, Andrew I. Su, and Dennis W. Wolan. Link: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4986259/
- 15.3.2017: Long non-coding RNA UCA1 promotes cisplatin/gemcitabine resistance through CREB modulating miR-196a-5p in bladder cancer cells by Jingjing Pan, Xu Li, Wenjing Wu, Mei Xue, Huilian Hou, Wen Zhai,Wei Chen. http://www.sciencedirect.com/science/article/pii/S0304383516304906
- 1.2.2017:Robust enumeration of cell subsets from tissue expression profiles by Aaron M Newman, Chih Long Liu, Michael R Green, Andrew J Gentles, Weiguo Feng, Yue Xu, Chuong D Hoang, Maximilian Diehn & Ash A Alizadeh. http://www.nature.com/nmeth/journal/v12/n5/full/nmeth.3337.html
Chen et al. 2015. Broad H3K4me3 is associated with increased transcription elongation and enhancer activity at tumor-suppressor genes. Nature Genetics. www.nature.com/ng/journal/v47/n10/full/ng.3385.html
Chen et Lv. 2016. Broad H3K4me3 as A Novel Epigenetic Signature for Normal Development and Disease. Genomics, Proteomics & Bioinformatics. www.sciencedirect.com/science/article/pii/S1672022916301395
Liu, Y., Ferguson, J. F., Xue, C., Silverman, I. M., Gregory, B., Reilly, M. P.,
& Li, M. (2013). Evaluating the impact of sequencing depth on transcriptome profiling in human adipose. PloS One, 8(6), e66883. http://doi.org/10.1371/journal.pone.0066883.
Wilson et al. Best Practices for Scientific Computing. PLOS Biology 2014. Online.
Qi Lv et al. Genome-wide protein-protein interactions and protein function exploration in cyanobacteria. Scientific Reports 2015. Online.
Kunitz et al. Comparative assessment of methods for the computational inference of transcript isoform abundance from RNA-seq data. Genome Biology 2015. Online.
Amanda J. Lea et al. A Flexible, Efficient Binomial Mixed Model for Identifying Differential DNA Methylation in Bisulfite Sequencing Data. PLOS Genetics 2015. http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1005650
Schwämmle et al. Assessment and Improvement of Statistical Tools for Comparative Proteomics Analysis of Sparse Data Sets with Few Experimental Replicates. J. Proteome Res., 2013. http://pubs.acs.org/doi/abs/10.1021/pr400045u
Nysia I George and Ching-Wei Chang. DAFS: a data-adaptive flag method for RNA-sequencing data to differentiate genes with low and high expression. BMC Bioinformatics 2014. www.biomedcentral.com/1471-2105/15/92
Bailey et al. Practical Guidelines for the Comprehensive Analysis of ChIP-seq Data. PLOS Computational Biology, Nov 2013. http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003326
Costello et al. A community effort to assess and improve drug sensitivity prediction algorithms. Nauture Biotechnology June 2014.
Law et al. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. GenomeBiology Feb 2014.
- Gaujoux & Seoighe. CellMix: a comprehensive toolbox for gene expression deconvolution. Bioinformatics Sep 2013. bioinformatics.oxfordjournals.org/content/29/17/2211.long
- Shen-Orr et al. Cell type–specific gene expression differences in complex tissues. Nat Methods Apr 2010. www.ncbi.nlm.nih.gov/pmc/articles/PMC3699332/;
Stockwell et al. DMAP: differential methylation analysis package for RRBS and WGBS data. Bioinformatics March 7, 2014.
Sims et al. Sequencing depth and coverage: key considerations in genomic analyses. Nature Reviews Genetics 15, 121–132 (2014)
Seyednasrollah et al. Comparison of software packages for detecting differential expression in RNA-seq studies. Brief Bioinform. 2013 Dec 2.
A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium.
SEQC/MAQC-III Consortium; SEQC/MAQC-III Consortium. Nat Biotechnol. 2014 Aug 24. doi: 10.1038/nbt.2957.