BackgroundRecent advances in sequencing technologies have greatly increased the identification of mutations in cancer genomes. However, it remains a significant challenge to identify cancer-driving mutations, since most observed missense changes are neutral passenger mutations. Various computational methods have been developed to predict the effects of amino acid substitutions on protein function and classify mutations as deleterious or benign. These include approaches that rely on evolutionary conservation, structural constraints, or physicochemical attributes of amino acid substitutions. Here we review existing methods and further examine eight tools: SIFT, PolyPhen2, Condel, CHASM, mCluster, logRE, SNAP, and MutationAssessor, with respect to their coverage, accuracy, availability and dependence on other tools.ResultsSingle nucleotide polymorphisms with high minor allele frequencies were used as a negative (neutral) set for testing, and recurrent mutations from the COSMIC database as well as novel recurrent somatic mutations identified in very recent cancer studies were used as positive (non-neutral) sets. Conservation-based methods generally had moderately high accuracy in distinguishing neutral from deleterious mutations, whereas the performance of machine learning based predictors with comprehensive feature spaces varied between assessments using different positive sets. MutationAssessor consistently provided the highest accuracies. For certain combinations metapredictors slightly improved the performance of included individual methods, but did not outperform MutationAssessor as stand-alone tool.ConclusionsOur independent assessment of existing tools reveals various performance disparities. Cancer-trained methods did not improve upon more general predictors. No method or combination of methods exceeds 81% accuracy, indicating there is still significant room for improvement for driver mutation prediction, and perhaps more sophisticated feature integration is needed to develop a more robust tool.

BackgroundIn an intercross between the SWR/J and BALB/c mouse strains, the pulmonary adenoma progression 1 (Papg1) locus on chromosome 4 modulates lung tumor size, one of several measures of lung tumor progression. This locus has not been fully characterized and defined in its extent and genetic content. Fine mapping of this and other loci affecting lung tumor phenotype is possible using recombinant inbred strains.ResultsA population of 376 mice, obtained by crossing mice of the SWR/J strain with CXBN recombinant inbred mice, was treated with a single dose of urethane and assayed for multiplicity of large lung tumors (N2lung). A genome-wide analysis comparing N2lung with 6364 autosomal SNPs revealed multiple peaks of association. The Papg1 locus had two peaks, at rs3654162 (70.574 Mb, -logP=2.8) and rs6209043 (86.606 Mb, -logP=2.7), joined by an interval of weaker statistical association; these data confirm the presence of Papg1 on chromosome 4 and reduce the mapping region to two stretches of ~6.8 and ~4.2 Mb, in the proximal and distal peaks, respectively. The distal peak included Cdkn2a, a gene already proposed as being involved in Papg1 function. Other loci possibly modulating N2lung were detected on chromosomes 5, 8, 9, 11, 15, and 19, but analysis for linkage disequilibrium of these putative loci with Papg1 locus suggested that only those on chromosomes 11 and 15 were true positives.ConclusionsThese findings suggest that Papg1 consists, most likely, of two distinct, nearby loci, and point to putative additional loci on chromosomes 11 and 15 modulating lung tumor size. Within Papg1, Cdkn2a appears to be a strong candidate gene while additional Papg1 genes await to be identified. Greater knowledge of the genetic and biochemical mechanisms underlying the germ-line modulation of lung tumor size in mice is relevant to other species, including humans, in that it may help identify new therapeutic targets in the fight against tumor progression.

BackgroundCooperia oncophora and Ostertagia ostertagi are among the most important gastrointestinal nematodes of cattle worldwide. The economic losses caused by these parasites are on the order of hundreds of millions of dollars per year. Conventional treatment of these parasites is through anthelmintic drugs; however, as resistance to anthelmintics increases, overall effectiveness has begun decreasing. New methods of control and alternative drug targets are necessary. In-depth analysis of transcriptomic data can help provide these targets.ResultsThe assembly of 8.7 million and 11 million sequences from C. oncophora and O. ostertagi, respectively, resulted in 29,900 and 34,792 transcripts. Among these, 69% and 73% of the predicted peptides encoded by C. oncophora and O. ostertagi had homologues in other nematodes. Approximately 21% and 24% were constitutively expressed in both species, respectively; however, the numbers of transcripts that were stage specific were much smaller (~1% of the transcripts expressed in a stage). Approximately 21% of the transcripts in C. oncophora and 22% in O. ostertagi were up-regulated in a particular stage. Functional molecular signatures were detected for 46% and 35% of the transcripts in C. oncophora and O. ostertagi, respectively. More in-depth examinations of the most prevalent domains led to knowledge of gene expression changes between the free-living (egg, L1, L2 and L3 sheathed) and parasitic (L3 exsheathed, L4, and adult) stages. Domains previously implicated in growth and development such as chromo domains and the MADF domain tended to dominate in the free-living stages. In contrast, domains potentially involved in feeding such as the zinc finger and CAP domains dominated in the parasitic stages. Pathway analyses showed significant associations between life-cycle stages and peptides involved in energy metabolism in O. ostertagi whereas metabolism of cofactors and vitamins were specifically up-regulated in the parasitic stages of C. oncophora. Substantial differences were observed also between Gene Ontology terms associated with free-living and parasitic stages.ConclusionsThis study characterized transcriptomes from multiple life stages from both C. oncophora and O. ostertagi. These data represent an important resource for studying these parasites. The results of this study show distinct differences in the genes involved in the free-living and parasitic life cycle stages. The data produced will enable better annotation of the upcoming genome sequences and will allow future comparative analyses of the biology, evolution and adaptation to parasitism in nematodes.

BackgroundRecent studies had found thousands of natural antisense transcripts originating from the same genomic loci of protein coding genes but from the opposite strand. It is unclear whether the majority of antisense transcripts are functional or merely transcriptional noise.ResultsUsing the Affymetrix Exon array with a modified cDNA synthesis protocol that enables genome-wide detection of antisense transcription, we conducted large-scale expression analysis of antisense transcripts in nine corresponding tissues from human, mouse and rat. We detected thousands of antisense transcripts, some of which show tissue-specific expression that could be subjected to further study for their potential function in the corresponding tissues/organs. The expression patterns of many antisense transcripts are conserved across species, suggesting selective pressure on these transcripts. When compared to protein-coding genes, antisense transcripts show a lesser degree of expression conservation. We also found a positive correlation between the sense and antisense expression across tissues.ConclusionOur results suggest that natural antisense transcripts are subjected to selective pressure but to a lesser degree compared to sense transcripts in mammals.

BackgroundMultiple myeloma (MM) is a malignant proliferation of plasma B cells. Based on recurrent aneuploidy such as copy number alterations (CNAs), myeloma is divided into two subtypes with different CNA patterns and patient survival outcomes. How aneuploidy events arise, and whether they contribute to cancer cell evolution are actively studied. The large amount of transcriptomic changes resultant of CNAs (dosage effect) pose big challenges for identifying functional consequences of CNAs in myeloma in terms of specific driver genes and pathways. In this study, we hypothesize that gene-wise dosage effect varies as a result from complex regulatory networks that translate the impact of CNAs to gene expression, and studying this variation can provide insights into functional effects of CNAs.ResultsWe propose gene-wise dosage effect score and genome-wide karyotype plot as tools to measure and visualize concordant copy number and expression changes across cancer samples. We find that dosage effect in myeloma is widespread yet variable, and it is correlated with gene expression level and CNA frequencies in different chromosomes. Our analysis suggests that despite the enrichment of differentially expressed genes between hyperdiploid MM and non-hyperdiploid MM in the trisomy chromosomes, the chromosomal proportion of dosage sensitive genes is higher in the non-trisomy chromosomes. Dosage-sensitive genes are enriched by genes with protein translation and localization functions, and dosage resistant genes are enriched by apoptosis genes. These results point to future studies on differential dosage sensitivity and resistance of pro- and anti-proliferation pathways and their variation across patients as therapeutic targets and prognosis markers.ConclusionsOur findings support the hypothesis that recurrent CNAs in myeloma are selected by their functional consequences. The novel dosage effect score defined in this work will facilitate integration of copy number and expression data for identifying driver genes in cancer genomics studies. The accompanying R code is available at http://www.canevolve.org/dosageEffect/.

BackgroundThere is a lack of understanding the evolutionary forces driving niche segregation of closely related organisms. In addition, pinpointing the genes driving ecological divergence is a key goal in molecular ecology. Here, larval transcriptome sequences obtained by next-generation-sequencing are used to address these issues in a morphologically cryptic sister species pair of non-biting midges (Chironomus riparius and C. piger).ResultsMore than eight thousand orthologous open reading frames were screened for interspecific divergence and intraspecific polymorphisms. Despite a small mean sequence divergence of 1.53% between the sister species, 25.1% of 18,115 observed amino acid substitutions were inferred by α statistics to be driven by positive selection. Applying McDonald-Kreitman tests to 715 alignments of gene orthologues identified eleven (1.5%) genes driven by positive selection.ConclusionsThree candidate genes were identified as potentially responsible for the observed niche segregation concerning nitrite concentration, habitat temperature and water conductivity. Additionally, signs of positive selection in the hydrogen sulfide detoxification pathway were detected, providing a new plausible hypothesis for the species’ ecological differentiation. Finally, a divergently selected, nuclear encoded mitochondrial ribosomal protein may contribute to reproductive isolation due to cytonuclear coevolution.