gapFinisher: a reliable gap filling pipeline for SSPACE-LongRead scaffolder output

Unknown sequences, or gaps, are largely present in most published genomes across public databases. Gap filling is an important finishing step in de novo genome assembly, especially in large genomes. The gap filling problem is nontrivial and while many computational tools exist partially solving the problem, several have shortcomings as to the reliability and correctness of the output, i.e. the gap filled draft genome. SSPACE-LongRead is a scaffolding software that utilizes long reads from multiple third-generation sequencing platforms in finding links between contigs and combining them. The long reads potentially contain sequence information to fill the gaps, but SSPACE-LongRead currently lacks this functionality. We present an automated pipeline called gapFinisher to process SSPACE-LongRead output to fill gaps after the actual scaffolding. gapFinisher is based on controlled use of a gap filling tool called FGAP and works on all standard Linux/UNIX command lines. We conclude that performing the workflows of SSPACE-LongRead and gapFinisher enables users to fill gaps reliably. There is no need for further scrutiny of the existing sequencing data after performing the analysis.

gapFinisher: a reliable gap filling pipeline for SSPACE-LongRead scaffolder output

Unknown sequences, or gaps, are largely present in most published genomes across public databases. Gap filling is an important finishing step in de novo genome assembly, especially in large genomes. The gap filling problem is nontrivial and while many computational tools exist partially solving the problem, several have shortcomings as to the reliability and correctness of the output, i.e. the gap filled draft genome. SSPACE-LongRead is a scaffolding software that utilizes long reads from multiple third-generation sequencing platforms in finding links between contigs and combining them. The long reads potentially contain sequence information to fill the gaps, but SSPACE-LongRead currently lacks this functionality. We present an automated pipeline called gapFinisher to process SSPACE-LongRead output to fill gaps after the actual scaffolding. gapFinisher is based on controlled use of a gap filling tool called FGAP and works on all standard Linux/UNIX command lines. We conclude that performing the workflows of SSPACE-LongRead and gapFinisher enables users to fill gaps reliably. There is no need for further scrutiny of the existing sequencing data after performing the analysis.

Transcriptome sequencing reveals high isoform diversity in the ant Formica exsecta

Transcriptome resources for social insects have the potential to provide new insight into polyphenism, i.e., how divergent phenotypes arise from the same genome. Here we present a transcriptome based on paired-end RNA sequencing data for the ant Formica exsecta (Formicidae, Hymenoptera). The RNA sequencing libraries were constructed from samples of several life stages of both sexes and female castes of queens and workers, in order to maximize representation of expressed genes. We first compare the performance of common assembly and scaffolding software (Trinity, Velvet-Oases, and SOAPdenovo-trans), in producing de novo assemblies. Second, we annotate the resulting expressed contigs to the currently published genomes of ants, and other insects, including the honeybee, to filter genes that have annotation evidence of being true genes. Our pipeline resulted in a final assembly of altogether 39,262 mRNA transcripts, with an average coverage of >300X, belonging to 17,496 unique genes with annotation in the related ant species. From these genes, 536 genes were unique to one caste or sex only, highlighting the importance of comprehensive sampling. Our final assembly also showed expression of several splice variants in 6,975 genes, and we show that accounting for splice variants affects the outcome of downstream analyses such as gene ontologies. Our transcriptome provides an outstanding resource for future genetic studies on F. exsecta and other ant species, and the presented transcriptome assembly can be adapted to any non-model species that has genomic resources available from a related taxon.

Emerging Technologies for Autonomous Language Learning

Drawing on a lengthier review completed for the US National Institute for Literacy, this paper examines emerging technologies that are applicable to self-access and autonomous learning in the areas of listening and speaking, collaborative writing, reading and language structure, and online interaction. Digital media reviewed include podcasts, blogs, wikis, online writing sites, text-scaffolding software, concordancers, multiuser virtual environments, multiplayer games, and chatbots. For each of these technologies, we summarize recent research and discuss possible uses for autonomous language learning.