Marsupials and monotremes sort genome treasures from junk

A recent landmark paper demonstrates the unique contribution of marsupials and monotremes to comparative genome analysis, filling an evolutionary gap between the eutherian mammals (including humans) and more distant vertebrate species

Transcript length bias in RNA-seq data confounds systems biology

BACKGROUND: Several recent studies have demonstrated the effectiveness of deep sequencing for transcriptome analysis (RNA-seq) in mammals. As RNA-seq becomes more affordable, whole genome transcriptional profiling is likely to become the platform of choice for species with good genomic sequences. As yet, a rigorous analysis methodology has not been developed and we are still in the stages of exploring the features of the data. RESULTS: We investigated the effect of transcript length bias in RNA-seq data using three different published data sets. For standard analyses using aggregated tag counts for each gene, the ability to call differentially expressed genes between samples is strongly associated with the length of the transcript. CONCLUSION: Transcript length bias for calling differentially expressed genes is a general feature of current protocols for RNA-seq technology. This has implications for the ranking of differentially expressed genes, and in particular may introduce bias in gene set testing for pathway analysis and other multi-gene systems biology analyses. REVIEWERS: This article was reviewed by Rohan Williams (nominated by Gavin Huttley), Nicole Cloonan (nominated by Mark Ragan) and James Bullard (nominated by Sandrine Dudoit)

Marsupials and monotremes sort genome treasures from junk

A recent landmark paper demonstrates the unique contribution of marsupials and monotremes to comparative genome analysis, filling an evolutionary gap between the eutherian mammals (including humans) and more distant vertebrate species

Gene ontology analysis for RNA-seq: accounting for selection bias

GOseq is a method for GO analysis of RNA-seq data that takes into account the length bias inherent in RNA-se

Vestige: Maximum likelihood phylogenetic footprinting

BACKGROUND: Phylogenetic footprinting is the identification of functional regions of DNA by their evolutionary conservation. This is achieved by comparing orthologous regions from multiple species and identifying the DNA regions that have diverged less than neutral DNA. Vestige is a phylogenetic footprinting package built on the PyEvolve toolkit that uses probabilistic molecular evolutionary modelling to represent aspects of sequence evolution, including the conventional divergence measure employed by other footprinting approaches. In addition to measuring the divergence, Vestige allows the expansion of the definition of a phylogenetic footprint to include variation in the distribution of any molecular evolutionary processes. This is achieved by displaying the distribution of model parameters that represent partitions of molecular evolutionary substitutions. Examination of the spatial incidence of these effects across regions of the genome can identify DNA segments that differ in the nature of the evolutionary process. RESULTS: Vestige was applied to a reference dataset of the SCL locus from four species and provided clear identification of the known conserved regions in this dataset. To demonstrate the flexibility to use diverse models of molecular evolution and dissect the nature of the evolutionary process Vestige was used to footprint the Ka/Ks ratio in primate BRCA1 with a codon model of evolution. Two regions of putative adaptive evolution were identified illustrating the ability of Vestige to represent the spatial distribution of distinct molecular evolutionary processes. CONCLUSION: Vestige provides a flexible, open platform for phylogenetic footprinting. Underpinned by the PyEvolve toolkit, Vestige provides a framework for visualising the signatures of evolutionary processes across the genome of numerous organisms simultaneously. By exploiting the maximum-likelihood statistical framework, the complex interplay between mutational processes, DNA repair and selection can be evaluated both spatially (along a sequence alignment) and temporally (for each branch of the tree) providing visual indicators to the attributes and functions of DNA sequences

Practical Issues in Imputation-Based Association Mapping

Imputation-based association methods provide a powerful framework for testing untyped variants for association with phenotypes and for combining results from multiple studies that use different genotyping platforms. Here, we consider several issues that arise when applying these methods in practice, including: (i) factors affecting imputation accuracy, including choice of reference panel; (ii) the effects of imputation accuracy on power to detect associations; (iii) the relative merits of Bayesian and frequentist approaches to testing imputed genotypes for association with phenotype; and (iv) how to quickly and accurately compute Bayes factors for testing imputed SNPs. We find that imputation-based methods can be robust to imputation accuracy and can improve power to detect associations, even when average imputation accuracy is poor. We explain how ranking SNPs for association by a standard likelihood ratio test gives the same results as a Bayesian procedure that uses an unnatural prior assumption—specifically, that difficult-to-impute SNPs tend to have larger effects—and assess the power gained from using a Bayesian approach that does not make this assumption. Within the Bayesian framework, we find that good approximations to a full analysis can be achieved by simply replacing unknown genotypes with a point estimate—their posterior mean. This approximation considerably reduces computational expense compared with published sampling-based approaches, and the methods we present are practical on a genome-wide scale with very modest computational resources (e.g., a single desktop computer). The approximation also facilitates combining information across studies, using only summary data for each SNP. Methods discussed here are implemented in the software package BIMBAM, which is available from http://stephenslab.uchicago.edu/software.html

A second-generation anchored genetic linkage map of the tammar wallaby (Macropus eugenii)

Background: \ud The tammar wallaby, Macropus eugenii, a small kangaroo used for decades for studies of reproduction and metabolism, is the model Australian marsupial for genome sequencing and genetic investigations. The production of a more comprehensive cytogenetically-anchored genetic linkage map will significantly contribute to the deciphering of the tammar wallaby genome. It has great value as a resource to identify novel genes and for comparative studies, and is vital for the ongoing genome sequence assembly and gene ordering in this species.\ud \ud Results: \ud A second-generation anchored tammar wallaby genetic linkage map has been constructed based on a total of 148 loci. The linkage map contains the original 64 loci included in the first-generation map, plus an additional 84 microsatellite loci that were chosen specifically to increase coverage and assist with the anchoring and orientation of linkage groups to chromosomes. These additional loci were derived from (a) sequenced BAC clones that had been previously mapped to tammar wallaby chromosomes by fluorescence in situ hybridization (FISH), (b) End sequence from BACs subsequently FISH-mapped to tammar wallaby chromosomes, and (c) tammar wallaby genes orthologous to opossum genes predicted to fill gaps in the tammar wallaby linkage map as well as three X-linked markers from a published study. Based on these 148 loci, eight linkage groups were formed. These linkage groups were assigned (via FISH-mapped markers) to all seven autosomes and the X chromosome. The sex-pooled map size is 1402.4 cM, which is estimated to provide 82.6% total coverage of the genome, with an average interval distance of 10.9 cM between adjacent markers. The overall ratio of female/male map length is 0.84, which is comparable to the ratio of 0.78 obtained for the first-generation map.\ud \ud Conclusions: \ud Construction of this second-generation genetic linkage map is a significant step towards complete coverage of the tammar wallaby genome and considerably extends that of the first-generation map. It will be a valuable resource for ongoing tammar wallaby genetic research and assembling the genome sequence. The sex-pooled map is available online at http://compldb.angis.org.au/

Secondary somatic mutations restoring RAD51C and RAD51D associated with acquired resistance to the PARP inhibitor rucaparib in high-grade ovarian carcinoma

High-grade epithelial ovarian carcinomas (OC) containing mutated BRCA1 or BRCA2 (BRCA1/2) homologous recombination (HR) genes are sensitive to platinum-based chemotherapy and poly(ADP-ribose) polymerase inhibitors (PARPi), while restoration of HR function due to secondary mutations in BRCA1/2 has been recognized as an important resistance mechanism. We sequenced core HR pathway genes in 12 pairs of pre-treatment and post-progression tumor biopsy samples collected from patients in ARIEL2 Part 1, a phase 2 study of the PARPi rucaparib as treatment for platinum-sensitive, relapsed OC. In six of 12 pre-treatment biopsies, a truncation mutation in BRCA1, RAD51C or RAD51D was identified. In five of six paired post-progression biopsies, one or more secondary mutations restored the open reading frame. Four distinct secondary mutations and spatial heterogeneity were observed for RAD51C. In vitro complementation assays and a patient-derived xenograft (PDX), as well as predictive molecular modeling, confirmed that resistance to rucaparib was associated with secondary mutations

Glucagonoma Masquerading as a Mucinous Cancer of the Ovary: Lessons from Cell Biology

High-grade mucinous ovarian cancer (HGMOC) is often a misnomer as the majority of cases are metastatic disease with a gastro-intestinal origin. The standard platinum-based ovarian cancer (OC) chemotherapy regimens are often ineffective, and there are insufficient data to support the use of colorectal cancer (CRC) chemotherapy regimens due to the rarity of HGMOC. We described a cohort of four consecutive suspected HGMOC cases treated at the Royal Women’s Hospital, Melbourne in 2012. Two cases were treated as primary MOC, whereas the other two were considered to be metastatic CRC based on histopathological and clinical evidence. From the RNAseq analysis, we identified two cases of HGMOC whose gene expression profiles were consistent with mucinous epithelial OC, one case that was treated as metastatic CRC with gene expression profile correlated with CRC and one case with neuroendocrine (NET) gene expression features. Interestingly, glucagon was over-expressed in this tumor that was subsequently confirmed by immunohistochemistry. These findings suggest a rare glucagonoma-like NET appendiceal tumor that had metastasized to the surface of ovary and were unresponsive to CRC chemotherapy regimens. In summary, a carefully curated panel of expression markers and selected functional genomics could provide diagnosis and treatment guidance for patients with possible HGMOC

PyCogent: a toolkit for making sense from sequence

The COmparative GENomic Toolkit, a framework for probabilistic analyses of biological sequences, devising workflows and generating publication quality graphics, has been implemented in Python