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Below are a lot of public datasets https://www.openml.org/search?type=data http://archive.ics.uci.edu/ml/datasets.php https://www.re3data.org/ https://www.data.gov/ https://www.kdnuggets.com/datasets/index.html http://dataportals.org/  

A Machine Learning Approach to DNA Shotgun Sequence Assembly. 2015. DNA FRAGMENT ASSEMBLY USING NEURAL PREDICTION TECHNIQUES. 1999. The main idea is to use NN for read prediction. For the reads with same prediction pattern, we cluster them into several parts and use Read more…

ASA Statement on the Role of Statistics in Data Science The rise of data science, including Big Data and data analytics, has recently attracted enormous attention in the popular press for its spectacular contributions in a wide range of scholarly Read more…

Volume 1 I have chosen two examples, contingency table analysis and causal inference, but I could have written similarly about the evolution of statistical thinking associated with time series analysis, again going back to early contributions of Yule), or about spatial statistics Read more…

GAML: genome assembly by maximum likelihood (2015) Bayesian Genome Assembly and Assessment by Markov Chain Monte Carlo Sampling (2014) ILP-based maximum likelihood genome scaffolding (2014) Toward a statistically explicit understanding of de novo sequence assembly (2013) CGAL: computing genome assembly likelihoods (2013) Denovo likelihood-based measures Read more…

GMcloser: closing gaps in assemblies accurately with a likelihood-based selection of contig or long-read alignments (2015) Mind the Gap: Upgrading Genomes with Pacific Biosciences RS Long-Read Sequencing Technology (2012) (PBJelly)

ABySS 2.0: resource-efficient assembly of large genomes using a Bloom filter (2017) departs from MPI and instead implements algorithms that employ a Bloom filter, a probabilistic data structure, to represent a de Bruijn graph and reduce memory requirements. We benchmarked Read more…

A comparative evaluation of genome assembly reconciliation tools (2017) benchmarked seven assembly reconciliation tools, namely CISA, GAA, GAM_NGS, GARM, Metassembler, MIX, and ZORRO Despite the inability of these assembly tools to solve the general assembly reconciliation problem, each tool demonstrated some strengths that could Read more…

SuRankCo: supervised ranking of contigs in de novo assemblies (2015) A machine learning approach to predict quality scores for contigs and to enable the ranking of contigs within an assembly. Information on characteristics of contigs from a de novo assembly are extracted Read more…