Dahlquist:BOSC ISMB 2016 Notes

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BOSC/NetBio SIG Day 1 2016-07-08

• Megan Crow spoke at NetBio Sig on Single-cell gene networks from co-expression
  • mentioned review on single cell RNA Seq, Grün, D., & van Oudenaarden, A. (2015). Design and analysis of single-cell sequencing experiments. Cell, 163(4), 799-810.
  • bar code RNAs before PCR amplification
• MultiQC: Aggregate results from bioinformatics analyses across many samples into a single report
• BOSC Keynote: Jennifer Gardy who is an Assistant Professor of Population and Public Health at the University of British Columbia and a Senior Scientist at the British Columbia Centre for Disease Control (BCCDC)
  • Slides: The open-source outbreak: can datga prevent the next pandemic?
• Blog summary of BOSC at GigaScience
• Docker

BOSC SIG Day 2 2016-07-09

• MetaR from Fabien Campagne lab
  • potentially collaborate on a grant; broader impacts of teaching MetaR to undergrads
• GenePattern Notebooks
  • an integrative analytical environment for genomic research; can do RNAseq analysis
  • available on Indiana University supercomputer cluster
• ReportMD
  • uses R markdown to generate linked HTML reports

ISMB Day 1 2016-07-10

• COSI/SIG SysMod (formerly BioPathways)
• iPath: Interactive Pathways Explorer
• Talk by Lars Juhl Jensen
  • STRING-DB, when comparing data from different protein-protein interaction data, need to calibrate raw quality scores against gold standard (KEGG), see von Mering et al 2005
  • text-mining snafu, human gene nomenclature committee approved human gene called "SDS"
• Talk by Natasa Przulj
  • http://www.nature.com/articles/srep04547 [Yaveroğlu, Ö. N., Malod-Dognin, N., Davis, D., Levnajic, Z., Janjic, V., Karapandza, R., ... & Pržulj, N. (2014). Revealing the hidden language of complex networks. Scientific reports, 4.]
    • small motifs/legos in networks
  • Nataša Pržulj, Noël Malod-Dognin (2016) Network analytics in the age of big data Science 08 Jul 2016:Vol. 353, Issue 6295, pp. 123-124 DOI: 10.1126/science.aah3449
• Oxford Journal Biology Methods and Protocols
  • talked to Jennifer Boyd at the booth and suggested the ability to update published protocols with new versions.
• Books
  • Gandrud, C. (2013). Chapman & Hall/CRC The R Series : Reproducible Research with R and R Studio. Bosa Roca, US: CRC Press. Retrieved from http://electra.lmu.edu:2110
    • library has e-book, but it looks like it is the first edition instead of second edition, limited to a certain number of pages
  • Korpelainen, E., Tuimala, J., & Somervuo, P. (2014). Chapman & Hall/CRC Mathematical and Computational Biology : RNA-seq Data Analysis : A Practical Approach. Boca Raton, US: Chapman and Hall/CRC.
    • library has e-book, limited to a certain number of pages.

ISMB Day 2 2016-07-11

Workshop on Education in Bioinformatics (WEB)

• Phil Bourne: The NIH Commons: A Cloud-based Training Environment
  • Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., ... & Bouwman, J. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific data, 3.
  • NIH Data Commons
  • Instead of funding individual investigators for computational infrastructure, give them credit to use NIH cloud instead, would include training.
• Nirav Merchant: How to Scale Science and People Using the Cloud (slides available after conference on ISMB app)
  • University of Arizona
  • "computational thinking had become central to our course work"
  • local, in-house computational expert: "victim of their own success (denial of service attacks on their availability)"
  • users want (chair), send them to home depot
    • Happy Birthday Ikea, here's your cake.x
  • Democratizing Innovation, Eric Von Hippel
  • need managed cloud: CyVerse Atmosphere, iPlant Collaborative, Jetstream
• Matthew Vaughn: Packaging computational biology tools for broad distribution and ease-of-reuse (need to get slides)
• Panel includes these three plus Annette McGrath of the Australian Bioinformatics Network

Talks

• Keynote: Sandrine Dudoit
  • SCONE - [R] - SCONE (Single-Cell Overview of Normalized Expression), a package for single-cell RNA-seq data quality control (QC) and normalization. This data-driven framework uses summaries of expression data to assess the efficacy of normalization workflows.f
  • SCONE one page description
  • GitHub repository
• David Gibbs: Solving the influence maximization problem on biological networks; a case study involving the cell cycle regulatory network in Saccharomyces cerevisiae
  • software on GitHub
  • Eser, P., Demel, C., Maier, K. C., Schwalb, B., Pirkl, N., Martin, D. E., ... & Tresch, A. (2014). Periodic mRNA synthesis and degradation co‐operate during cell cycle gene expression. Molecular systems biology, 10(1), 717.
  • BioFabric: Comb the Hairball
• Scott Simpkins: Scalable tools for quantitative analysis from sequencing-based chemical-genetic genetic interaction screens
  • BEAN-counter for processing barcode sequencing data from multiplexed experiments. Originally designed for chemical genomics experiments performed in the Myers/Boone Labs, it is applicable to any experiment in which pools of genetically barcoded cells are grown under different conditions, with the resulting barcode DNA isolated from those cells combined into one 2nd-gen sequencing run via the use of indexed PCR primers.
    • not open source, must obtain license from http://license.umn.edu/technologies/20170001_bean-counter-quantitative-scoring-of-chemical-genetic-interactions
    1. map index tag to condition
    2. parse fastq file
    3. interaction z score
    4. batch effect correction (unsupervised or ?)
    5. visualization
  • look out for "variance as a function of gene counts"

Posters

• O30 - Explicit Modeling of Differential RNA Stability Improves Inference of Transcription Regulation Networks by Konstantine Tchourine , NYU - Center for Genomics and Systems Biology
  • when using YEASTRACT data, require 1 direct and 2 direct evidence
  • Kemmeran KO dataset
  • Sameith, K., Amini, S., Koerkamp, M. J. G., van Leenen, D., Brok, M., Brabers, N., ... & Apweiler, E. (2015). A high-resolution gene expression atlas of epistasis between gene-specific transcription factors exposes potential mechanisms for genetic interactions. BMC biology, 13(1), 1.
  • Ma, S., Kemmeren, P., Gresham, D., & Statnikov, A. (2014). De-novo learning of genome-scale regulatory networks in S. cerevisiae. Plos one, 9(9), e106479.
  • Zheng, J., Benschop, J. J., Shales, M., Kemmeren, P., Greenblatt, J., Cagney, G., ... & Krogan, N. J. (2010). Epistatic relationships reveal the functional organization of yeast transcription factors. Molecular systems biology, 6(1), 420.
  • Neymotin, B., Athanasiadou, R., & Gresham, D. (2014). Determination of in vivo RNA kinetics using RATE-seq. Rna, 20(10), 1645-1652.--key paper
    • DTA, 4-thiouracil
  • Shalem et al. (2008) Molecular Systems Biology--I already have this paper
  • Munchel, S. E., Shultzaberger, R. K., Takizawa, N., & Weis, K. (2011). Dynamic profiling of mRNA turnover reveals gene-specific and system-wide regulation of mRNA decay. Molecular biology of the cell, 22(15), 2787-2795.
• O54 - AuPairWise: biologically focused RNA-seq quality control using co-expression by Sara Ballouz, Cold Spring Harbor Laboratory
  • published in Ballouz, S., & Gillis, J. (2016). AuPairWise: a method to estimate RNA-seq replicability through co-expression. PLoS Comput Biol, 12(4), e1004868.
  • mentions Seqc/Maqc-Iii Consortium. (2014). A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium. Nature biotechnology, 32(9), 903-914.
    • recommendations to discard low expressing genes (1/3 of measured dynamic range)
    • discard genes with lower log fold changes between conditions < log2FC 1~2
  • want self-correlation of replicates across conditions, but when replicates are not available, use co-expressed gene pairs
  • has list of housekeeping genes for humans
  • in discussion with another delegate, recommended a minimum of 7 replicates, randomized to reduce batch effects
• O60 - Title: FAIRDOM: Data and Model Management for all by Natalie Stanford, University of Manchester, United Kingdom of Great Britain and Northern Ireland
  • http://researchobjects.org/
  • http://fair-dom.org/
    • FAIR: Findable, Accessible, Interoperable, Reusable
• N14 - Benchmark Analysis of RNA-Seq Aligners by Gregory Grant, University of Pennsylvania
  • Sent me PDF of poster via e-mail
  • bottom line, Tophat performs poorly, but is the most popular alignment programs
• B20 - The impact of amplification on differential expression analyses by RNA-seq by Swati Parekh, Ludwig-Maximilians University Munich
  • Parekh, S., Ziegenhain, C., Vieth, B., Enard, W., & Hellmann, I. (2016). The impact of amplification on differential expression analyses by RNA-seq. Scientific reports, 6.
• E14 - Cross-platform normalization of microarray and RNA-seq data for machine learning applications by Jeffrey Thompson, Dartmouth College
  • [Jeffrey A. Thompson et al.. (2015). Training Distribution Matching (TDM) R Package. Zenodo. 10.5281/zenodo.32852 http://zenodo.org/record/32852#.V4TwGaJFqug]
    • note use of research/data sharing site that gives doi

Other notes

• Goblet: Global Organisation for Bioinformatics Learning, Education & Training
• Android 6 marshmallow allows you to deny access to apps (like location, etc.)
• CatterPlots!!!!!
• Conesa, A., Madrigal, P., Tarazona, S., Gomez-Cabrero, D., Cervera, A., McPherson, A., ... & Mortazavi, A. (2016). A survey of best practices for RNA-seq data analysis. Genome biology, 17(1), 1.
• List of articles in WikiProject Computational Biology