BACKGROUND: Recent advances in transcriptome sequencing have enabled the discovery of thousands of long non-coding RNAs (lncRNAs) across multitudes of species. Though several lncRNAs have been shown to play important roles in diverse biological processes, the functions and mechanisms of most lncRNAs remain unknown. Two significant obstacles lie between transcriptome sequencing and functional characterization of lncRNAs: 1) identifying truly noncoding genes from de novo reconstructed transcriptomes, and 2) prioritizing hundreds of resulting putative lncRNAs from each sample for downstream experimental interrogation. RESULTS: We present slncky, a computational lncRNA discovery tool that produces a high-quality set of lncRNAs from RNA-Sequencing data and further prioritizes lncRNAs by characterizing selective constraint as a proxy for function. Our filtering pipeline is comparable to manual curation efforts and more sensitive than previously published approaches. Further, we develop, for the first time, a sensitive alignment pipeline for aligning lncRNA loci and propose new evolutionary metrics relevant for both sequence and transcript evolution. Our analysis reveals that selection acts in several distinct patterns, and uncovers two notable classes of lncRNAs: one showing strong purifying selection at RNA sequence and another where constraint is restricted to the regulation but not the sequence of the transcript. CONCLUSION: Our novel comparative methods for lncRNAs reveals 233 constrained lncRNAs out of tens of thousands of currently annotated transcripts, which we believe should be prioritized for further interrogation. To aid in their analysis we provide the slncky Evolution Browser as a resource for experimentalists.

Custom sequence capture experiments are becoming an efficient approach for gathering large sets of orthologous markers with targeted levels of informativeness in non-model organisms. Transcriptome-based exon capture utilizes transcript sequences to design capture probes, often with the aid of a reference genome to identify intron-exon boundaries and exclude shorter exons (< 200 bp). Here, we test an alternative approach that directly uses transcript sequences for probe design, which are often composed of multiple exons of varying lengths. Based on a selection of 1,260 orthologous transcripts, we conducted sequence captures across multiple phylogenetic scales for frogs, including species up to ~100 million years divergent from the focal group. After several conservative filtering steps, we recovered a large phylogenomic data set consisting of sequence alignments for 1,047 of the 1,260 transcriptome-based loci (~630,000 bp) and a large quantity of highly variable regions flanking the exons in transcripts (~70,000 bp). We recovered high numbers of both shorter (< 100 bp) and longer exons (> 200 bp), with no major reduction in coverage towards the ends of exons. We observed significant differences in the performance of blocking oligos for target enrichment and non-target depletion during captures, and observed differences in PCR duplication rates that can be attributed to the number of individuals pooled for capture reactions. We explicitly tested the effects of phylogenetic distance on capture sensitivity, specificity, and missing data, and provide a baseline estimate of expectations for these metrics based on nuclear pairwise differences among samples. We provide recommendations for transcriptome-based exon capture design based on our results, and describe multiple pipelines for data assembly and analysis.

Whole-genome low-coverage sequencing has been combined with linkage-disequilibrium (LD) based genotype refinement to accurately and cost-effectively infer genotypes in large cohorts of individuals. Most genotype refinement methods are based on hidden Markov models, which are accurate but computationally expensive. We introduce an algorithm that models LD using a simple multivariate Gaussian distribution. The key feature of our algorithm is its speed, it is hundreds of times faster than other methods on the same data set and its scaling behaviour is linear in the number of samples. We demonstrate the performance of the method on both low-coverage and high-coverage samples.

The last 20 years of advancement in DNA sequencing technologies have led to the sequencing of thousands of microbial genomes, creating mountains of genetic data. While our efficiency in generating the data improves almost daily, applying meaningful relationships between the taxonomic and genetic entities on this scale requires a structured and integrative approach. Currently, the knowledge is distributed across a fragmented landscape of resources from government-funded institutions such as NCBI and UniProt to topic-focused databases like the ODB3 database of prokaryotic operons, to the supplemental table of a primary publication. A major drawback to large scale, expert curated databases is the expense of maintaining and extending them over time. No entity apart from a major institution with stable long term funding can consider this, and their scope is limited considering the magnitude of microbial data being generated daily. Wikidata is an, openly editable, semantic web compatible framework for knowledge representation. It is a project of the Wikimedia Foundation and offers knowledge integration capabilities ideally suited to the challenge of representing the exploding body of information about microbial genomics. We are developing a microbial specific data model, based on Wikidata’s semantic web compatibility, that represents bacterial species, strains and the gene and gene products that define them. Currently, we have loaded 1736 gene items and 1741 protein items for two strains of the human pathogenic bacteria Chlamydia trachomatis and used this subset of data as an example of the empowering utility of this model. In our next phase of development, we will expand by adding another 118 bacterial genomes and their gene and gene products, totaling over 900,000 additional entities. This aggregation of knowledge will be a platform for community-driven collaboration, allowing the networking of microbial genetic data through the sharing of knowledge by both the data and domain expert.

Natural selection may often favor coordination between different traits, or phenotypic integration, in order to most efficiently acquire and deploy scarce resources. As leaves are the primary photosynthetic organ in plants, many have proposed that leaf physiology, biochemistry, and anatomical structure are coordinated along a functional trait spectrum from fast, resource-acquisitive syndromes to slow, resource-conservative syndromes. However, the coordination hypothesis has rarely been tested at a phylogenetic scale most relevant for understanding rapid adaptation in the recent past or predicting evolutionary trajectories in response to climate change. To that end, we used a common garden to examine genetically-based coordination between leaf traits across 19 wild and cultivated tomato taxa. We found surprisingly weak integration between photosynthetic rate, leaf structure, biochemical capacity, and CO2 diffusion, even though all were arrayed in the predicted direction along a ‘fast-slow’ spectrum. This suggests considerable scope for unique trait combinations to evolve in response to new environments or in crop breeding. In particular, we find that partially independent variation in stomatal and mesophyll conductance may allow a plant to improve water-use efficiency without necessarily sacrificing maximum photosynthetic rates. Our study does not imply that functional trait spectra or tradeoffs are unimportant, but that the many important axes of variation within a taxonomic group may be unique and not generalizable to other taxa.

The capacity to map traits over large cohorts of individuals – phenomics – lags far behind the explosive development in genomics. For microbes the estimation of growth is the key phenotype. We introduce an automated microbial phenomics framework that delivers accurate and highly resolved growth phenotypes at an unprecedented scale. Advancements were achieved through introduction of transmissive scanning hardware and software technology, frequent acquisition of precise colony population size measurements, extraction of population growth rates from growth curves and removal of spatial bias by reference-surface normalization. Our prototype arrangement automatically records and analyses 100,000 experiments in parallel. We demonstrate the power of the approach by extending and nuancing the known salt defence biology in baker’s yeast. The introduced framework will have a transformative impact by providing high-quality microbial phenomics data for extensive cohorts of individuals and generating well-populated and standardized phenomics databases.

While implementing the algorithm, we discovered two mathematical mistakes in Gotoh’s paper that induce sub-optimal sequence alignments. First, there are minor indexing mistakes in the dynamic programming algorithm which become apparent immediately when implementing the procedure. Hence, we report on these for the sake of completeness. Second, there is a more profound problem with the dynamic programming matrix initialization. This initialization issue can easily be missed and find its way into actual implementations. This error is also present in standard text books. Namely, the widely used books by Gusfield and Waterman. To obtain an initial estimate of the extent to which this error has been propagated, we scrutinized freely available undergraduate lecture slides. We found that 8 out of 31 lecture slides contained the mistake, while 16 out of 31 simply omit parts of the initialization, thus giving an incomplete description of the algorithm. Finally, by inspecting ten source codes and running respective tests, we found that five implementations were incorrect. Note that, not all bugs we identified are due to the mistake in Gotoh’s paper. Three implementations rely on additional constraints that limit generality. Thus, only two out of ten yield correct results. We show that the error introduced by Gotoh is straightforward to resolve and provide a correct open-source reference implementation. We do believe though, that raising the awareness about these errors is critical, since the impact of incorrect pairwise sequence alignments that typically represent one of the very first stages in any bioinformatics data analysis pipeline can have a detrimental impact on downstream analyses such as multiple sequence alignment, orthology assignment, phylogenetic analyses, divergence time estimates, etc.

The observation that variants regulating gene expression (expression quantitative trait loci, eQTL) are at a high frequency among SNPs associated with complex traits has made the genome-wide characterization of gene expression an important tool in genetic mapping studies of such traits. As part of a study to identify genetic loci contributing to bipolar disorder and a wide range of BP-related quantitative traits in members of 26 pedigrees from Costa Rica and Colombia, we measured gene expression in lymphoblastoid cell lines derived from 786 pedigree members. The study design enabled us to comprehensively reconstruct the genetic regulatory network in these families, provide estimates of heritability, identify eQTL, evaluate missing heritability for the eQTL, and quantify the number of different alleles contributing to any given locus.