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Background: Although single molecule sequencing is still improving, the lengths of the generated sequences are inevitably an advantage in genome assembly. Prior work that utilizes long reads to conduct genome assembly has mostly focused on correcting sequencing errors and improving contiguity of de novo assemblies. Results: We propose a disassembling-reassembling approach for both correcting structural errors in the draft assembly and scaffolding a target assembly based on error-corrected single molecule sequences. To achieve this goal, we formulate a maximum alternating path cover problem. We prove that this problem is NP-hard, and solve it by a 2-approximation algorithm. Conclusions: Our experimental results show that our approach can improve the structural correctness of target assemblies in the cost of some contiguity, even with smaller amounts of long reads. In addition, our reassembling process can also serve as a competitive scaffolder relative to well-established assembly benchmarks. [ABSTRACT FROM AUTHOR]

The advent of next generation sequencing technology has generated massive amounts of biological data at unprecendented rates. Comparative genomics applications often require compute-intensive tools for subsequent analysis of high throughput data. Although cloud computing infrastructure plays an important role in this respect, the pressure from such computationally expensive tasks can be further alleviated using efficient data partitioning and workflow fusion. Here, we implement a workflow-based model for parallelizing the data-intensive tasks of genome alignment and variant calling with BWA and GATK's HaplotypeCaller. We explore three different approaches of partitioning data, granularity-based, individual-based, and alignment-based , and how each affect the run time. We observe granularity-based partitioning for BWA and alignment-based partitioning for HaplotypeCaller to be the optimal choices for the pipeline. We further discuss the methods and impact of workflow fusion on performance by considering different levels of fusion and how it affects our results. We identify the various open problems encountered, such as understanding the extent of parallelism, using heterogenous environments without a shared file system, and determining the granularity of inputs, and provide insights into addressing them. Finally, we report significant performance improvements, from 12 days to under 2 hours while running the BWA-GATK pipeline using partitioning and fusion. [ABSTRACT FROM AUTHOR]

Background: Transcriptome sequencing and assembly represent a great resource for the study of non-model species, and many metrics have been used to evaluate and compare these assemblies. Unfortunately, it is still unclear which of these metrics accurately reflect assembly quality. Results: We simulated sequencing transcripts of Drosophila melanogaster. By assembling these simulated reads using both a "perfect" and a modern transcriptome assembler while varying read length and sequencing depth, we evaluated quality metrics to determine whether they 1) revealed perfect assemblies to be of higher quality, and 2) revealed perfect assemblies to be more complete as data quantity increased. Several commonly used metrics were not consistent with these expectations, including average contig coverage and length, though they became consistent when singletons were included in the analysis. We found several annotation-based metrics to be consistent and informative, including contig reciprocal best hit count and contig unique annotation count. Finally, we evaluated a number of novel metrics such as reverse annotation count, contig collapse factor, and the ortholog hit ratio, discovering that each assess assembly quality in unique ways. Conclusions: Although much attention has been given to transcriptome assembly, little research has focused on determining how best to evaluate assemblies, particularly in light of the variety of options available for read length and sequencing depth. Our results provide an important review of these metrics and give researchers tools to produce the highest quality transcriptome assemblies. [ABSTRACT FROM AUTHOR]

Recent sequencing efforts have targeted the gene-rich regions of the maize (Zea mayst L.) genome. We report the release of an improved assembly of maize assembled genesis islands (MAGI's). 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 MAGI's are correctly assembled. Because the rates at which junction-testing PCR primers for genesis survey sequences (90-92%) amplify genesis DNA are not significantly different from those of control primers (≈9l%), 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 ≈33,000 and ≈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. [ABSTRACT FROM AUTHOR]

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]

Arthropods play a dominant role in natural and human-modified terrestrial ecosystem dynamics. Spatially-explicit arthropod population time-series data are crucial for statistical or mathematical models of these dynamics and assessment of their veterinary, medical, agricultural, and ecological impacts. Such data have been collected world-wide for over a century, but remain scattered and largely inaccessible. In particular, with the ever-present and growing threat of arthropod pests and vectors of infectious diseases, there are numerous historical and ongoing surveillance efforts, but the data are not reported in consistent formats and typically lack sufficient metadata to make reuse and re-analysis possible. Here, we present the first-ever minimum information standard for arthropod abundance, Minimum Information for Reusable Arthropod Abundance Data (MIReAD). Developed with broad stakeholder collaboration, it balances sufficiency for reuse with the practicality of preparing the data for submission. It is designed to optimize data (re)usability from the "FAIR," (Findable, Accessible, Interoperable, and Reusable) principles of public data archiving (PDA). This standard will facilitate data unification across research initiatives and communities dedicated to surveillance for detection and control of vector-borne diseases and pests. [ABSTRACT FROM AUTHOR]