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The human genome reference assembly is crucial for aligning and analyzing sequence data, and for genome annotation, among other roles. However, the models and analysis assumptions that underlie the current assembly need revising to fully represent human sequence diversity. Improved analysis tools and updated data reporting formats are also required.

Many tumors are composed of genetically divergent cell subpopulations. We report SubcloneSeeker, a package capable of exhaustive identification of subclone structures and evolutionary histories with bulk somatic variant allele frequency measurements from tumor biopsies. We present a statistical framework to elucidate whether specific sets of mutations are present within the same subclones, and the order in which they occur. We demonstrate how subclone reconstruction provides crucial information about tumorigenesis and relapse mechanisms; guides functional study by variant prioritization, and has the potential as a rational basis for informed therapeutic strategies for the patient. SubcloneSeeker is available at: https://github.com/yiq/SubcloneSeeker.

Background: Different classes of haplotype block algorithms exist and the ideal dataset to assess their performance would be to comprehensively re-sequence a large genomic region in a large population. Such data sets are expensive to collect. Alternatively, we performed coalescent simulations to generate haplotypes with a high marker density and compared block partitioning results from diversity based, LD based, and information theoretic algorithms under different values of SNP density and allele frequency., Results: We simulated 1000 haplotypes using the standard coalescent for three world populations--European, African American, and East Asian--and applied three classes of block partitioning algorithms--diversity based, LD based, and information theoretic. We assessed algorithm differences in number, size, and coverage of blocks inferred under different conditions of SNP density, allele frequency, and sample size. Each algorithm inferred blocks differing in number, size, and coverage under different density and allele frequency conditions. Different partitions had few if any matching block boundaries. However they still overlapped and a high percentage of total chromosomal region was common to all methods. This percentage was generally higher with a higher density of SNPs and when rarer markers were included., Conclusion: A gold standard definition of a haplotype block is difficult to achieve, but collecting haplotypes covered with a high density of SNPs, partitioning them with a variety of block algorithms, and identifying regions common to all methods may be the best way to identify genomic regions that harbor SNP variants that cause disease.

Circulating tumor-derived cell-free DNA (ctDNA) enables non-invasive diagnosis, monitoring, and treatment susceptibility testing in human cancers. However, accurate detection of variant alleles, particularly during untargeted searches, remains a principal obstacle to widespread application of cell-free DNA in clinical oncology. In this study, isolation of short cell-free DNA fragments is shown to enrich for tumor variants and improve correction of PCR- and sequencing-associated errors. Subfractions of the mononucleosome of circulating cell-free DNA (ccfDNA) were isolated from patients with melanoma, pancreatic ductal adenocarcinoma, and colorectal adenocarcinoma using a high-throughput-capable automated gel-extraction platform. Using a 128-gene (128 kb) custom next-generation sequencing panel, variant alleles were on average 2-fold enriched in the short fraction (median insert size: ~142 bp) compared to the original ccfDNA sample, while 0.7-fold reduced in the fraction corresponding to the principal peak of the mononucleosome (median insert size: ~167 bp). Size-selected short fractions compared to the original ccfDNA yielded significantly larger family sizes (i.e., PCR duplicates) during in silico consensus sequence interpretation via unique molecular identifiers. Increments in family size were associated with a progressive reduction of PCR and sequencing errors. Although consensus read depth also decreased at larger family sizes, the variant allele frequency in the short ccfDNA fraction remained consistent, while variant detection in the original ccfDNA was commonly lost at family sizes necessary to minimize errors. These collective findings support the automated extraction of short ccfDNA fragments to enrich for ctDNA while concomitantly reducing false positives through in silico error correction. [ABSTRACT FROM AUTHOR]

MOSAIK is a stable, sensitive and open-source program for mapping second and third-generation sequencing reads to a reference genome. Uniquely among current mapping tools, MOSAIK can align reads generated by all the major sequencing technologies, including Illumina, Applied Biosystems SOLiD, Roche 454, Ion Torrent and Pacific BioSciences SMRT. Indeed, MOSAIK was the only aligner to provide consistent mappings for all the generated data (sequencing technologies, low-coverage and exome) in the 1000 Genomes Project. To provide highly accurate alignments, MOSAIK employs a hash clustering strategy coupled with the Smith-Waterman algorithm. This method is well-suited to capture mismatches as well as short insertions and deletions. To support the growing interest in larger structural variant (SV) discovery, MOSAIK provides explicit support for handling known-sequence SVs, e.g. mobile element insertions (MEIs) as well as generating outputs tailored to aid in SV discovery. All variant discovery benefits from an accurate description of the read placement confidence. To this end, MOSAIK uses a neural-network based training scheme to provide well-calibrated mapping quality scores, demonstrated by a correlation coefficient between MOSAIK assigned and actual mapping qualities greater than 0.98. In order to ensure that studies of any genome are supported, a training pipeline is provided to ensure optimal mapping quality scores for the genome under investigation. MOSAIK is multi-threaded, open source, and incorporated into our command and pipeline launcher system GKNO (http://gkno.me). [ABSTRACT FROM AUTHOR]

Motivation: A common question arises at the beginning of every experiment where RNA-Seq is used to detect differential gene expression between two conditions: How many reads should we sequence?Results: Scotty is an interactive web-based application that assists biologists to design an experiment with an appropriate sample size and read depth to satisfy the user-defined experimental objectives. This design can be based on data available from either pilot samples or publicly available datasets.Availability: Scotty can be freely accessed on the web at http://euler.bc.edu/marthlab/scotty/scotty.phpContact: gabor.marth@bc.eduSupplementary information: Supplementary data are is available at Bioinformatics online. [ABSTRACT FROM AUTHOR]