A first genome assembly of the barley fungal pathogen Pyrenophora teres f. teres

Background: Pyrenophora teres f. teres is a necrotrophic fungal pathogen and the cause of one of barley’s most important diseases, net form of net blotch. Here we report the first genome assembly for this species based solely on short Solexa sequencing reads of isolate 0-1. The assembly was validated by comparison to BAC sequences, ESTs, orthologous genes and by PCR, and complemented by cytogenetic karyotyping and the first genome-wide genetic map for P. teres f. teres. Results: The total assembly was 41.95 Mbp and contains 11,799 gene models of 50 amino acids or more. Comparison against two sequenced BACs showed that complex regions with a high GC content assembled effectively. Electrophoretic karyotyping showed distinct chromosomal polymorphisms between isolates 0-1 and 15A, and cytological karyotyping confirmed the presence of at least nine chromosomes. The genetic map spans 2477.7 cM and is composed of 243 markers in 25 linkage groups, and incorporates SSR markers developed from the assembly. Among predicted genes, non-ribosomal peptide synthetases and efflux pumps in particular appear to have undergone a P. teres f. teres-specific expansion of non-orthologous gene families. Conclusions: This study demonstrates that paired-end Solexa sequencing can successfully capture coding regions of a filamentous fungal genome. The assembly contains a plethora of predicted genes that have been implicated in a necrotrophic lifestyle and pathogenicity and presents a significant resource for examining the bases for P. teres f. teres pathogenicity

A first genome assembly of the barley fungal pathogen Pyrenophora teres f. teres

Background: Pyrenophora teres f. teres is a necrotrophic fungal pathogen and the cause of one of barley’s most important diseases, net form of net blotch. Here we report the first genome assembly for this species based solely on short Solexa sequencing reads of isolate 0-1. The assembly was validated by comparison to BAC sequences, ESTs, orthologous genes and by PCR, and complemented by cytogenetic karyotyping and the first genome-wide genetic map for P. teres f. teres. Results: The total assembly was 41.95 Mbp and contains 11,799 gene models of 50 amino acids or more. Comparison against two sequenced BACs showed that complex regions with a high GC content assembled effectively. Electrophoretic karyotyping showed distinct chromosomal polymorphisms between isolates 0-1 and 15A, and cytological karyotyping confirmed the presence of at least nine chromosomes. The genetic map spans 2477.7 cM and is composed of 243 markers in 25 linkage groups, and incorporates SSR markers developed from the assembly. Among predicted genes, non-ribosomal peptide synthetases and efflux pumps in particular appear to have undergone a P. teres f. teres-specific expansion of non-orthologous gene families. Conclusions: This study demonstrates that paired-end Solexa sequencing can successfully capture coding regions of a filamentous fungal genome. The assembly contains a plethora of predicted genes that have been implicated in a necrotrophic lifestyle and pathogenicity and presents a significant resource for examining the bases for P. teres f. teres pathogenicity

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical science. © The Author(s) 2019. Published by Oxford University Press