PyPop: a mature open-source software pipeline for population genomics

Python for Population Genomics (PyPop) is a software package that processes genotype and allele data and performs large-scale population genetic analyses on highly polymorphic multi-locus genotype data. In particular, PyPop tests data conformity to Hardy-Weinberg equilibrium expectations, performs Ewens-Watterson tests for selection, estimates haplotype frequencies, measures linkage disequilibrium, and tests significance. Standardized means of performing these tests is key for contemporary studies of evolutionary biology and population genetics, and these tests are central to genetic studies of disease association as well. Here, we present PyPop 1.0.0, a new major release of the package, which implements new features using the more robust infrastructure of GitHub, and is distributed via the industry-standard Python Package Index. New features include implementation of the asymmetric linkage disequilibrium measures and, of particular interest to the immunogenetics research communities, support for modern nomenclature, including colon-delimited allele names, and improvements to meta-analysis features for aggregating outputs for multiple populations. Code available at: https://zenodo.org/records/10080668 and https://github.com/alexlancaster/pypop.

A new paradigm for the scientific enterprise: nurturing the ecosystem

The institutions of science are in a state of flux. Declining public funding for basic science, the increasingly corporatized administration of universities, increasing "adjunctification" of the professoriate and poor academic career prospects for postdoctoral scientists indicate a significant mismatch between the reality of the market economy and expectations in higher education for science. Solutions to these issues typically revolve around the idea of fixing the career "pipeline", which is envisioned as being a pathway from higher-education training to a coveted permanent position, and then up a career ladder until retirement. In this paper, we propose and describe the term "ecosystem" as a more appropriate way to conceptualize today's scientific training and the professional landscape of the scientific enterprise. First, we highlight the issues around the concept of "fixing the pipeline". Then, we articulate our ecosystem metaphor by describing a series of concrete design patterns that draw on peer-to-peer, decentralized, cooperative, and commons-based approaches for creating a new dynamic scientific enterprise.

Multi-tissue transcriptomics of the black widow spider reveals expansions, co-options, and functional processes of the silk gland gene toolkit

Background

Spiders (Order Araneae) are essential predators in every terrestrial ecosystem largely because they have evolved potent arsenals of silk and venom. Spider silks are high performance materials made almost entirely of proteins, and thus represent an ideal system for investigating genome level evolution of novel protein functions. However, genomic level resources remain limited for spiders.

Results

We de novo assembled a transcriptome for the Western black widow (Latrodectus hesperus) from deeply sequenced cDNAs of three tissue types. Our multi-tissue assembly contained ~100,000 unique transcripts, of which > 27,000 were annotated by homology. Comparing transcript abundance among the different tissues, we identified 647 silk gland-specific transcripts, including the few known silk fiber components (e.g. six spider fibroins, spidroins). Silk gland specific transcripts are enriched compared to the entire transcriptome in several functions, including protein degradation, inhibition of protein degradation, and oxidation-reduction. Phylogenetic analyses of 37 gene families containing silk gland specific transcripts demonstrated novel gene expansions within silk glands, and multiple co-options of silk specific expression from paralogs expressed in other tissues.

Conclusions

We propose a transcriptional program for the silk glands that involves regulating gland specific synthesis of silk fiber and glue components followed by protecting and processing these components into functional fibers and glues. Our black widow silk gland gene repertoire provides extensive expansion of resources for biomimetic applications of silk in industry and medicine. Furthermore, our multi-tissue transcriptome facilitates evolutionary analysis of arachnid genomes and adaptive protein systems.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-365) contains supplementary material, which is available to authorized users.

HLA-A, -B, -C, and -DRB1 allele and haplotype frequencies distinguish Eastern European Americans from the general European American population

Sequence-based typing was used to identify human leukocyte antigen (HLA)-A, -B, -C, and -DRB1 alleles from 558 consecutively recruited US volunteers with Eastern European ancestry for an unrelated hematopoietic stem cell registry. Four of 31 HLA-A alleles, 29 HLA-C alleles, 59 HLA-B alleles, and 42 HLA-DRB1 alleles identified (A*0325, B*440204, Cw*0332, and *0732N) are novel. The HLA-A*02010101g allele was observed at a frequency of 0.28. Two-, three-, and four-locus haplotypes were estimated using the expectation-maximization algorithm. The highest frequency extended haplotypes (A*010101g-Cw*070101g-B*0801g-DRB1*0301 and A*03010101g-Cw*0702-B*0702-DRB1*1501) were observed at frequencies of 0.04 and 0.03, respectively. Linkage disequilibrium values (Dij') of the constituent two-locus haplotypes were highly significant for both extended haplotypes (P values were less than 8 x 10(-10)) but were consistently higher for the more frequent haplotype. Balancing selection was inferred to be acting on all the four loci, with the strongest evidence of balancing selection observed for the HLA-C locus. Comparisons of the A-C-B haplotypes and DRB1 frequencies in this population with those for African, European, and western Asian populations showed high degrees of identity with Czech, Polish, and Slovenian populations and significant differences from the general European American population.

PyPop update--a software pipeline for large-scale multilocus population genomics

Population genetic statistics from multilocus genotype data inform our understanding of the patterns of genetic variation and their implications for evolutionary studies, generally, and human disease studies in particular. In any given population one can estimate haplotype frequencies, identify deviation from Hardy-Weinberg equilibrium, test for balancing or directional selection, and investigate patterns of linkage disequilibrium. Existing software packages are oriented primarily toward the computation of such statistics on a population-by-population basis, not on comparisons among populations and across different statistics. We developed PyPop (Python for Population Genomics) to facilitate the analyses of population genetic statistics across populations and the relationships among different statistics within and across populations. PyPop is an open-source framework for performing large-scale population genetic analyses on multilocus genotype data. It computes the statistics described above, among others. PyPop deploys a standard Extensible Markup Language (XML) output format and can integrate the results of multiple analyses on various populations that were performed at different times into a common output format that can be read into a spreadsheet. The XML output format allows PyPop to be embedded as part of a larger analysis pipeline. Originally developed to analyze the highly polymorphic genetic data of the human leukocyte antigen region of the human genome, PyPop has applicability to any kind of multilocus genetic data. It is the primary analysis platform for analyzing data collected for the Anthropological component of the 13th and 14th International Histocompatibility Workshops. PyPop has also been successfully used in studies by our group, with collaborators, and in publications by several independent research teams.