BACKGROUND: The high error rate associated with Oxford Nanopore sequencing technology adversely affects demultiplexing. To improve demultiplexing and reduce unclassified reads from nanopore sequencing data, we developed MysteryMaster, a demultiplexer that utilizes the optimal sequence aligner, Cola. RESULTS: When compared to Oxford Nanopore´s Dorado and Guppy demultiplexing tools across three datasets of 37 diverse samples with established ground truth, we found that MysteryMaster accurately identifies a similar or greater percentage of reads among the different basecalling models: Fast, HAC, and SUP. MysteryMaster performs slightly better than the other tools on data that was basecalled using the Fast basecalled model, while its performance in HAC and SUP data is similar to Dorado's. MysteryMaster has a false positive rate of just 0.41% with default settings. CONCLUSIONS: While MysteryMaster can function as a standalone demultiplexer tool, the sequential application of Dorado and MysteryMaster produced the best overall performance.

BACKGROUND: Generating polygenic risk scores for diseases and complex traits requires high quality GWAS summary statistic files. Often, these files can be difficult to acquire either as a result of unshared or incomplete data. To date, bioinformatics tools which focus on restoring missing columns containing identification and association data are limited, which has the potential to increase the number of usable GWAS summary statistics files. RESULTS: SumStatsRehab was able to restore rsID, effect/other alleles, chromosome, base pair position, effect allele frequencies, beta, standard error, and p-values to a better extent than any other currently available tool, with minimal loss. CONCLUSIONS: SumStatsRehab offers a unique tool utilizing both functional programming and pipeline-like architecture, allowing users to generate accurate data restorations for incomplete summary statistics files. This in turn, increases the number of usable GWAS summary statistics files, which may be invaluable for less researched health traits.

BACKGROUND: A common challenge in bioinformatics is to identify short sub-sequences that are unique in a set of genomes or reference sequences, which can efficiently be achieved by k-mer (k consecutive nucleotides) counting. However, there are several areas that would benefit from a more stringent definition of "unique", requiring that these sub-sequences of length W differ by more than k mismatches (i.e. a Hamming distance greater than k) from any other sub-sequence, which we term the k-disjoint problem. Examples include finding sequences unique to a pathogen for probe-based infection diagnostics; reducing off-target hits for re-sequencing or genome editing; detecting sequence (e.g. phage or viral) insertions; and multiple substitution mutations. Since both sensitivity and specificity are critical, an exhaustive, yet efficient solution is desirable. RESULTS: We present microTaboo, a method that allows for efficient and extensive sequence mining of unique (k-disjoint) sequences of up to 100 nucleotides in length. On a number of simulated and real data sets ranging from microbe- to mammalian-size genomes, we show that microTaboo is able to efficiently find all sub-sequences of a specified length W that do not occur within a threshold of k mismatches in any other sub-sequence. We exemplify that microTaboo has many practical applications, including point substitution detection, sequence insertion detection, padlock probe target search, and candidate CRISPR target mining. CONCLUSIONS: microTaboo implements a solution to the k-disjoint problem in an alignment- and assembly free manner. microTaboo is available for Windows, Mac OS X, and Linux, running Java 7 and higher, under the GNU GPLv3 license, at: https://MohammedAlJaff.github.io/microTaboo.

BACKGROUND: DNA methylation plays a key role in developmental processes, which is reflected in changing methylation patterns at specific CpG sites over the lifetime of an individual. The underlying mechanisms are complex and possibly affect multiple genes or entire pathways. RESULTS: We applied a multivariate approach to identify combinations of CpG sites that undergo modifications when transitioning between developmental stages. Monte Carlo feature selection produced a list of ranked and statistically significant CpG sites, while rule-based models allowed for identifying particular methylation changes in these sites. Our rule-based classifier reports combinations of CpG sites, together with changes in their methylation status in the form of easy-to-read IF-THEN rules, which allows for identification of the genes associated with the underlying sites. CONCLUSION: We utilized machine learning and statistical methods to discretize decision class (age) values to get a general pattern of methylation changes over the lifespan. The CpG sites present in the significant rules were annotated to genes involved in brain formation, general development, as well as genes linked to cancer and Alzheimer's disease.

UNLABELLED: Whiteboard is a class library implemented in C++ that enables visualization to be tightly coupled with computation when analyzing large and complex datasets. AVAILABILITY AND IMPLEMENTATION: the C++ source code, coding samples and documentation are freely available under the Lesser General Public License from http://whiteboard-class.sourceforge.net/.

BACKGROUND: Genomic duplications constitute major events in the evolution of species, allowing paralogous copies of genes to take on fine-tuned biological roles. Unambiguously identifying the orthology relationship between copies across multiple genomes can be resolved by synteny, i.e. the conserved order of genomic sequences. However, a comprehensive analysis of duplication events and their contributions to evolution would require all-to-all genome alignments, which increases at N2 with the number of available genomes, N. RESULTS: Here, we introduce Kraken, software that omits the all-to-all requirement by recursively traversing a graph of pairwise alignments and dynamically re-computing orthology. Kraken scales linearly with the number of targeted genomes, N, which allows for including large numbers of genomes in analyses. We first evaluated the method on the set of 12 Drosophila genomes, finding that orthologous correspondence computed indirectly through a graph of multiple synteny maps comes at minimal cost in terms of sensitivity, but reduces overall computational runtime by an order of magnitude. We then used the method on three well-annotated mammalian genomes, human, mouse, and rat, and show that up to 93% of protein coding transcripts have unambiguous pairwise orthologous relationships across the genomes. On a nucleotide level, 70 to 83% of exons match exactly at both splice junctions, and up to 97% on at least one junction. We last applied Kraken to an RNA-sequencing dataset from multiple vertebrates and diverse tissues, where we confirmed that brain-specific gene family members, i.e. one-to-many or many-to-many homologs, are more highly correlated across species than single-copy (i.e. one-to-one homologous) genes. Not limited to protein coding genes, Kraken also identifies thousands of newly identified transcribed loci, likely non-coding RNAs that are consistently transcribed in human, chimpanzee and gorilla, and maintain significant correlation of expression levels across species. CONCLUSIONS: Kraken is a computational genome coordinate translator that facilitates cross-species comparisons, distinguishes orthologs from paralogs, and does not require costly all-to-all whole genome mappings. Kraken is freely available under LPGL from http://github.com/nedaz/kraken.

MOTIVATION: Comparative genomics heavily relies on alignments of large and often complex DNA sequences. From an engineering perspective, the problem here is to provide maximum sensitivity (to find all there is to find), specificity (to only find real homology) and speed (to accommodate the billions of base pairs of vertebrate genomes). RESULTS: Satsuma addresses all three issues through novel strategies: (i) cross-correlation, implemented via fast Fourier transform; (ii) a match scoring scheme that eliminates almost all false hits; and (iii) an asynchronous 'battleship'-like search that allows for aligning two entire fish genomes (470 and 217 Mb) in 120 CPU hours using 15 processors on a single machine. AVAILABILITY: Satsuma is part of the Spines software package, implemented in C++ on Linux. The latest version of Spines can be freely downloaded under the LGPL license from http://www.broadinstitute.org/science/programs/genome-biology/spines/.