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Synonymous codons were originally viewed as interchangeable, with no phenotypic consequences. However, substantial evidence has now demonstrated that synonymous substitutions can perturb a variety of gene expression and protein homeostasis mechanisms, including translational efficiency, translational fidelity, and cotranslational folding of the encoded protein. To date, most studies of synonymous codon-derived perturbations have focused on effects within a single gene. Here, we show that synonymous codon substitutions made far within the coding sequence of Escherichia coli plasmid-encoded chloramphenicol acetyltransferase (cat) can significantly increase expression of the divergent upstream tetracycline resistance gene, tetR. In four out of nine synonymously recoded cat sequences tested, expression of the upstream tetR gene was significantly elevated due to transcription of a long antisense RNA (asRNA) originating from a transcription start site within cat. Surprisingly, transcription of this asRNA readily bypassed the native tet transcriptional repression mechanism. Even more surprisingly, accumulation of the TetR protein correlated with the level of asRNA, rather than total tetR RNA. These effects of synonymous codon substitutions on transcription and translation of a neighboring gene suggest that synonymous codon usage in bacteria may be under selection to both preserve the amino acid sequence of the encoded gene and avoid DNA sequence elements that can significantly perturb expression of neighboring genes. Avoiding such sequences may be especially important in plasmids and prokaryotic genomes, where genes and regulatory elements are often densely packed. Similar considerations may apply to the design of genetic circuits for synthetic biology applications. [ABSTRACT FROM AUTHOR]

The fall armyworm, Spodoptera frugiperda (J. E. Smith), is a highly polyphagous pest native to the tropical Americas that has recently spread to become a global super-pest threatening food and fiber production. Transgenic crops producing insecticidal Cry and Vip3Aa proteins from Bacillus thuringiensis (Bt) are used for control of this pest in its native range. The evolution of practical resistance represents the greatest threat to sustainability of this technology and its potential efficacy in the S. frugiperda invasive range. Monitoring for resistance is vital to management approaches delaying S. frugiperda resistance to Bt crops. DNA-based resistance screening provides higher sensitivity and cost-effectiveness than currently used bioassay-based monitoring. So far, practical S. frugiperda resistance to Bt corn-producing Cry1F has been genetically linked to mutations in the SfABCC2 gene, providing a model to develop and test monitoring tools. In this study, we performed targeted SfABCC2 sequencing followed by Sanger sequencing to confirm the detection of known and candidate resistance alleles to Cry1F corn in field-collected S. frugiperda from continental USA, Puerto Rico, Africa (Ghana, Togo, and South Africa), and Southeast Asia (Myanmar). Results confirm that the distribution of a previously characterized resistance allele (SfABCC2mut) is limited to Puerto Rico and identify 2 new candidate SfABCC2 alleles for resistance to Cry1F, one of them potentially spreading along the S. frugiperda migratory route in North America. No candidate resistance alleles were found in samples from the invasive S. frugiperda range. These results provide support for the potential use of targeted sequencing in Bt resistance monitoring programs. [ABSTRACT FROM AUTHOR]

Phlebotomine sand flies are of global significance as important vectors of human disease, transmitting bacterial, viral, and protozoan pathogens, including the kinetoplastid parasites of the genus Leishmania, the causative agents of devastating diseases collectively termed leishmaniasis. More than 40 pathogenic Leishmania species are transmitted to humans by approximately 35 sand fly species in 98 countries with hundreds of millions of people at risk around the world. No approved efficacious vaccine exists for leishmaniasis and available therapeutic drugs are either toxic and/or expensive, or the parasites are becoming resistant to the more recently developed drugs. Therefore, sand fly and/or reservoir control are currently the most effective strategies to break transmission. To better understand the biology of sand flies, including the mechanisms involved in their vectorial capacity, insecticide resistance, and population structures we sequenced the genomes of two geographically widespread and important sand fly vector species: Phlebotomus papatasi, a vector of Leishmania parasites that cause cutaneous leishmaniasis, (distributed in Europe, the Middle East and North Africa) and Lutzomyia longipalpis, a vector of Leishmania parasites that cause visceral leishmaniasis (distributed across Central and South America). We categorized and curated genes involved in processes important to their roles as disease vectors, including chemosensation, blood feeding, circadian rhythm, immunity, and detoxification, as well as mobile genetic elements. We also defined gene orthology and observed micro-synteny among the genomes. Finally, we present the genetic diversity and population structure of these species in their respective geographical areas. These genomes will be a foundation on which to base future efforts to prevent vector-borne transmission of Leishmania parasites. Author summary: The leishmaniases are a group of neglected tropical diseases caused by protist parasites from the Genus Leishmania. Different Leishmania species present a wide clinical profile, ranging from mild, often self-resolving cutaneous lesions that can lead to protective immunity, to severe metastatic mucosal disease, to visceral disease that is ultimately fatal. Leishmania parasites are transmitted by the bites of sand flies, and as no approved human vaccine exists, available drugs are toxic and/or expensive and parasite resistance to them is emerging, new dual control strategies to combat these diseases must be developed, combining interventions on human infections and integrated sand fly population management. Effective vector control requires a comprehensive understanding of the biology of sand flies. To this end, we sequenced and annotated the genomes of two sand fly species that are important leishmaniasis vectors from the Old and New Worlds. These genomes allow us to better understand, at the genetic level, processes important in the vector biology of these species, such as finding hosts, blood-feeding, immunity, and detoxification. These genomic resources highlight the driving forces of evolution of two major Leishmania vectors and provide foundations for future research on how to better prevent leishmaniasis by control of the sand fly vectors. [ABSTRACT FROM AUTHOR]

Species distributed across heterogeneous environments often evolve locally adapted ecotypes, but understanding of the genetic mechanisms involved in their formation and maintenance in the face of gene flow is incomplete. In Burkina Faso, the major African malaria mosquito Anopheles funestus comprises two strictly sympatric and morphologically indistinguishable yet karyotypically differentiated forms reported to differ in ecology and behavior. However, knowledge of the genetic basis and environmental determinants of An. funestus diversification was impeded by lack of modern genomic resources. Here, we applied deep whole-genome sequencing and analysis to test the hypothesis that these two forms are ecotypes differentially adapted to breeding in natural swamps versus irrigated rice fields. We demonstrate genomewide differentiation despite extensive microsympatry, synchronicity, and ongoing hybridization. Demographic inference supports a split only ~1,300 y ago, closely following the massive expansion of domesticated African rice cultivation ~1,850 y ago. Regions of highest divergence, concentrated in chromosomal inversions, were under selection during lineage splitting, consistent with local adaptation. The origin of nearly all variations implicated in adaptation, including chromosomal inversions, substantially predates the ecotype split, suggesting that rapid adaptation was fueled mainly by standing genetic variation. Sharp inversion frequency differences likely facilitated adaptive divergence between ecotypes by suppressing recombination between opposing chromosomal orientations of the two ecotypes, while permitting free recombination within the structurally monomorphic rice ecotype. Our results align with growing evidence from diverse taxa that rapid ecological diversification can arise from evolutionarily old structural genetic variants that modify genetic recombination. [ABSTRACT FROM AUTHOR]

Protein structural classification (PSC) is a supervised problem of assigning proteins into pre‐defined structural (e.g., CATH or SCOPe) classes based on the proteins' sequence or 3D structural features. We recently proposed PSC approaches that model protein 3D structures as protein structure networks (PSNs) and analyze PSN‐based protein features, which performed better than or comparable to state‐of‐the‐art sequence or other 3D structure‐based PSC approaches. However, existing PSN‐based PSC approaches model the whole 3D structure of a protein as a static (i.e., single‐layer) PSN. Because folding of a protein is a dynamic process, where some parts (i.e., sub‐structures) of a protein fold before others, modeling the 3D structure of a protein as a PSN that captures the sub‐structures might further help improve the existing PSC performance. Here, we propose to model 3D structures of proteins as multi‐layer sequential PSNs that approximate 3D sub‐structures of proteins, with the hypothesis that this will improve upon the current state‐of‐the‐art PSC approaches that are based on single‐layer PSNs (and thus upon the existing state‐of‐the‐art sequence and other 3D structural approaches). Indeed, we confirm this on 72 datasets spanning ~44 000 CATH and SCOPe protein domains. [ABSTRACT FROM AUTHOR]

As an ancient segmental tetraploid, the maize (Zea mays L.) genome contains large numbers of paralogs that are expected to have diverged by a minimum of 10% over time. Nearly/dentical paralogs (NIPs) are defined as paralogous genes that exhibit [greater than or equal to] 98% identity. Sequence analyses of the 'gene space' of the maize inbred line B73 genome, coupled with wet lab validation, have revealed that, conservatively, at least ~1% of maize genes have a NIP, a rate substantially higher than that in Arabidopsis. In most instances, both members of maize NIP pairs are expressed and are therefore at least potentially functional. Of evolutionary significance, members of many NIP families also exhibit differential expression. The finding that some families of maize NIPs are closely linked genetically while others are genetically unlinked is consistent with multiple modes of origin. NIPs provide a mechanism for the maize genome to circumvent the inherent limitation that diploid genomes can carry at most two 'alleles' per 'locus.' As such, NIPs may have played important roles during the evolution and domestication of maize and may contribute to the success of long-term selection experiments in this important crop species.

Recent sequencing efforts have targeted the gene-rich regions of the maize (Zea mays L.) genome. We report the release of an improved assembly of maize assembled genomic islands (MAGIs). The 114,173 resulting contigs have been subjected to computational and physical quality assessments. Comparisons to the sequences of maize bacterial artificial chromosomes suggest that at least 97% (160 of 165) of MAGIs are correctly assembled. Because the rates at which junction-testing PCR primers for genomic survey sequences (90-92%) amplify genomic DNA are not significantly different from those of control primers ([approximately equal to] 91%), we conclude that a very high percentage of genic MAGIs accurately reflect the structure of the maize genome. EST alignments, ab initio gene prediction, and sequence similarity searches of the MAGIs are available at the Iowa State University MAGI web site. This assembly contains 46,688 ab initio predicted genes. The expression of almost half (628 of 1,369) of a sample of the predicted genes that lack expression evidence was validated by RT-PCR. Our analyses suggest that the maize genome contains between [approximately equal to] 33,000 and [approximately equal to] 54,000 expressed genes. Approximately 5% (32 of 628) of the maize transcripts discovered do not have detectable paralogs among maize ESTs or detectable homologs from other species in the GenBank NR nucleotide/protein database. Analyses therefore suggest that this assembly of the maize genome contains approximately 350 previously uncharacterized expressed genes. We hypothesize that these 'orphans' evolved quickly during maize evolution and/or domestication. assembly validation | gene prediction | maize genome assembly | nearly identical paralog

There is a growing appreciation that synonymous codon usage, although historically regarded as phenotypically silent, can instead alter a wide range of mechanisms related to functional protein production, a term we use here to describe the net effect of transcription (mRNA synthesis), mRNA half‐life, translation (protein synthesis) and the probability of a protein folding correctly to its active, functional structure. In particular, recent discoveries have highlighted the important role that sub‐optimal codons can play in modifying co‐translational protein folding. These results have drawn increased attention to the patterns of synonymous codon usage within coding sequences, particularly in light of the discovery that these patterns can be conserved across evolution for homologous proteins. Because synonymous codon usage differs between organisms, for heterologous gene expression it can be desirable to make synonymous codon substitutions to match the codon usage pattern from the original organism in the heterologous expression host. Here we present CHARMING (for Codon HARMonizING), a robust and versatile algorithm to design mRNA sequences for heterologous gene expression and other related codon harmonization tasks. CHARMING can be run as a downloadable Python script or via a web portal at http://www.codons.org. [ABSTRACT FROM AUTHOR]

Abstract Background Alternative arrangements of chromosome 2 inversions in Anopheles gambiae are important sources of population structure, and are associated with adaptation to environmental heterogeneity. The forces responsible for their origin and maintenance are incompletely understood. Molecular characterization of inversion breakpoints provides insight into how they arose, and provides the basis for development of molecular karyotyping methods useful in future studies. Methods Sequence comparison of regions near the cytological breakpoints of 2Rb allowed the molecular delineation of breakpoint boundaries. Comparisons were made between the standard 2R+b arrangement in the An. gambiae PEST reference genome and the inverted 2Rb arrangements in the An. gambiae M and S genome assemblies. Sequence differences between alternative 2Rb arrangements were exploited in the design of a PCR diagnostic assay, which was evaluated against the known chromosomal banding pattern of laboratory colonies and field-collected samples from Mali and Cameroon. Results The breakpoints of the 7.55 Mb 2Rb inversion are flanked by extensive runs of the same short (72 bp) tandemly organized sequence, which was likely responsible for chromosomal breakage and rearrangement. Application of the molecular diagnostic assay suggested that 2Rb has a single common origin in An. gambiae and its sibling species, Anopheles arabiensis, and also that the standard arrangement (2R+b) may have arisen twice through breakpoint reuse. The molecular diagnostic was reliable when applied to laboratory colonies, but its accuracy was lower in natural populations. Conclusions The complex repetitive sequence flanking the 2Rb breakpoint region may be prone to structural and sequence-level instability. The 2Rb molecular diagnostic has immediate application in studies based on laboratory colonies, but its usefulness in natural populations awaits development of complementary molecular tools.

Abstract Background Complexity and noise in expression quantitative trait loci (eQTL) studies make it difficult to distinguish potential regulatory relationships among the many interactions. The predominant method of identifying eQTLs finds associations that are significant at a genome-wide level. The vast number of statistical tests carried out on these data make false negatives very likely. Corrections for multiple testing error render genome-wide eQTL techniques unable to detect modest regulatory effects. We propose an alternative method to identify eQTLs that builds on traditional approaches. In contrast to genome-wide techniques, our method determines the significance of an association between an expression trait and a locus with respect to the set of all associations to the expression trait. The use of this specific information facilitates identification of expression traits that have an expression profile that is characterized by a single exceptional association to a locus. Our approach identifies expression traits that have exceptional associations regardless of the genome-wide significance of those associations. This property facilitates the identification of possible false negatives for genome-wide significance. Further, our approach has the property of prioritizing expression traits that are affected by few strong associations. Expression traits identified by this method may warrant additional study because their expression level may be affected by targeting genes near a single locus. Results We demonstrate our method by identifying eQTL hotspots in Plasmodium falciparum (malaria) and Saccharomyces cerevisiae (yeast). We demonstrate the prioritization of traits with few strong genetic effects through Gene Ontology (GO) analysis of Yeast. Our results are strongly consistent with results gathered using genome-wide methods and identify additional hotspots and eQTLs. Conclusions New eQTLs and hotspots found with this method may represent regions of the genome or biological processes that are controlled through few relatively strong genetic interactions. These points of interest warrant experimental investigation.

Abstract Background Several recent studies have demonstrated the use of Roche 454 sequencing technology for de novo transcriptome analysis. Low error rates and high coverage also allow for effective SNP discovery and genetic diversity estimates. However, genetically diverse datasets, such as those sourced from natural populations, pose challenges for assembly programs and subsequent analysis. Further, estimating the effectiveness of transcript discovery using Roche 454 transcriptome data is still a difficult task. Results Using the Roche 454 FLX Titanium platform, we sequenced and assembled larval transcriptomes for two butterfly species: the Propertius duskywing, Erynnis propertius (Lepidoptera: Hesperiidae) and the Anise swallowtail, Papilio zelicaon (Lepidoptera: Papilionidae). The Expressed Sequence Tags (ESTs) generated represent a diverse sample drawn from multiple populations, developmental stages, and stress treatments. Despite this diversity, > 95% of the ESTs assembled into long (> 714 bp on average) and highly covered (> 9.6× on average) contigs. To estimate the effectiveness of transcript discovery, we compared the number of bases in the hit region of unigenes (contigs and singletons) to the length of the best match silkworm (Bombyx mori) protein--this "ortholog hit ratio" gives a close estimate on the amount of the transcript discovered relative to a model lepidopteran genome. For each species, we tested two assembly programs and two parameter sets; although CAP3 is commonly used for such data, the assemblies produced by Celera Assembler with modified parameters were chosen over those produced by CAP3 based on contig and singleton counts as well as ortholog hit ratio analysis. In the final assemblies, 1,413 E. propertius and 1,940 P. zelicaon unigenes had a ratio > 0.8; 2,866 E. propertius and 4,015 P. zelicaon unigenes had a ratio > 0.5. Conclusions Ultimately, these assemblies and SNP data will be used to generate microarrays for ecoinformatics examining climate change tolerance of different natural populations. These studies will benefit from high quality assemblies with few singletons (less than 26% of bases for each assembled transcriptome are present in unassembled singleton ESTs) and effective transcript discovery (over 6,500 of our putative orthologs cover at least 50% of the corresponding model silkworm gene).

Chromosomal inversions can lead to reproductive isolation and adaptation in insects such as Drosophila melanogaster and the non-model malaria vector Anopheles gambiae. Inversions can be detected and characterized using principal component analysis (PCA) of single nucleotide polymorphisms (SNPs). To aid in developing such methods, we formed a new benchmark derived from three publicly-available insect data. We then used this benchmark to perform an extended validation of our software for inversion analysis (Asaph). Through that process, we identified and characterized several problematic test cases liable to misinterpretation that can help guide PCA-based inversion detection. Lastly, we re-analyzed the 2R chromosome arm of 150 An. gambiae and coluzzii samples and observed two inversions (2Rc and 2Rd) that were previously known but not annotated in these particular individuals. The resulting benchmark data set and methods will be useful for future inversion detection based solely on SNP data. [ABSTRACT FROM AUTHOR]

Motivation Most amino acids are encoded by multiple synonymous codons, some of which are used more rarely than others. Analyses of positions of such rare codons in protein sequences revealed that rare codons can impact co-translational protein folding and that positions of some rare codons are evolutionarily conserved. Analyses of their positions in protein 3-dimensional structures, which are richer in biochemical information than sequences alone, might further explain the role of rare codons in protein folding. Results We model protein structures as networks and use network centrality to measure the structural position of an amino acid. We first validate that amino acids buried within the structural core are network-central, and those on the surface are not. Then, we study potential differences between network centralities and thus structural positions of amino acids encoded by conserved rare, non-conserved rare and commonly used codons. We find that in 84% of proteins, the three codon categories occupy significantly different structural positions. We examine protein groups showing different codon centrality trends, i.e. different relationships between structural positions of the three codon categories. We see several cases of all proteins from our data with some structural or functional property being in the same group. Also, we see a case of all proteins in some group having the same property. Our work shows that codon usage is linked to the final protein structure and thus possibly to co-translational protein folding. Availability and implementation https://nd.edu/∼cone/CodonUsage/. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Improved computational modeling of protein translation rates, including better prediction of where translational slowdowns along an mRNA sequence may occur, is critical for understanding co-translational folding. Because codons within a synonymous codon group are translated at different rates, many computational translation models rely on analyzing synonymous codons. Some models rely on genome-wide codon usage bias (CUB), believing that globally rare and common codons are the most informative of slow and fast translation, respectively. Others use the CUB observed only in highly expressed genes, which should be under selective pressure to be translated efficiently (and whose CUB may therefore be more indicative of translation rates). No prior work has analyzed these models for their ability to predict translational slowdowns. Here, we evaluate five models for their association with slowly translated positions as denoted by two independent ribosome footprint (RFP) count experiments from S. cerevisiae, because RFP data is often considered as a "ground truth" for translation rates across mRNA sequences. We show that all five considered models strongly associate with the RFP data and therefore have potential for estimating translational slowdowns. However, we also show that there is a weak correlation between RFP counts for the same genes originating from independent experiments, even when their experimental conditions are similar. This raises concerns about the efficacy of using current RFP experimental data for estimating translation rates and highlights a potential advantage of using computational models to understand translation rates instead. [ABSTRACT FROM AUTHOR]

As a specific type of structural variation, inversions are enjoying particular traction as a result of their established role in evolution. Using third-generation sequencing technology to predict inversions is growing in interest, but many such methods focus on improving sensitivity, giving rise to either too many false positives or very long running times. In this paper, we propose a new framework for inversion detection based on a combination of two novel theoretical models: rectangle clustering and representative rectangle prediction. This combination can automatically filter out false positive inversion predictions while retaining correct ones, leading to a method that has both high sensitivity and high positive prediction values (PPV). Further, this new framework can run very fast on available data. Our software can be freely obtained at https://github.com/UTbioinf/RigInv. [ABSTRACT FROM AUTHOR]

High-throughput sequencing techniques have generated massive quantities of genotype data. Haplotype phasing has proven to be a useful and effective method for analyzing these data. However, the quality of phasing is undermined due to missing information. Imputation provides an effective means of improving the underlying genotype information. For model organisms, imputation can rely on an available reference genotype panel and a physical or genetic map. For non-model organisms, which often do not have a genotype panel, it is important to design an imputation technique that does not rely on reference data. Here, we present Accurate Data-Driven Imputation Technique (ADDIT), which is composed of two data-driven algorithms capable of handling data generated from model and non-model organisms. The non-model variant of ADDIT (referred to as ADDIT-NM) employs statistical inference methods to impute missing genotypes, whereas the model variant (referred to as ADDIT-M) leverages a supervised learning-based approach for imputation. We demonstrate that both variants of ADDIT are more accurate, faster, and require less memory than leading state-of-the-art imputation tools using model (human) and non-model (maize, apple, and grape) genotype data. Software Availability: The source code of ADDIT and test data sets are available at https://github.com/NDBL/ADDIT. [ABSTRACT FROM AUTHOR]

Association tests performed with the Likelihood-Ratio Test (LR Test) can be an alternative to F S T , which is often used in population genetics to find variants of interest. Because the LR Test has several properties that could make it preferable to F S T , we propose a novel approach for modeling unknown genotypes in highly-similar species. To show the effectiveness of this LR Test approach, we apply it to single-nucleotide polymorphisms (SNPs) associated with the recent speciation of the malaria vectors Anopheles gambiae and Anopheles coluzzii and compare to F S T . [ABSTRACT FROM AUTHOR]