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Diversity within science, technology, engineering, and mathematics (STEM) remains disturbingly low. Relative to larger, highly funded universities, smaller schools harbor more diverse student demographics and more limited resources. Here, we propose four strategies leveraging the unique advantages of smaller institutions to advance underrepresented scholars along STEM pathways., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Songbirds have an unusual genomic element which is only found in their germline cells, known as the germline-restricted chromosome (GRC). Because germ cells contain both GRC and non-GRC (or A-chromosome) sequences, confidently identifying the GRC-derived elements from genome assemblies has proven difficult. Here, we introduce a new application of a transcriptomic method for GRC sequence identification. By adapting the Stringtie/Ballgown pipeline to use somatic and germline DNA reads, we find that the ratio of fragments per kilobase per million mapped reads can be used to confidently assign contigs to the GRC. Using this comparative coverage analysis, we successfully identify 733 contigs as high confidence GRC sequences (720 newly identified in this study) and 51 contigs which were validated using quantitative polymerase chain reaction. We also identified two new GRC genes, one hypothetical protein and one gene encoding an RNase H-like domain, and placed 16 previously identified but unplaced genes onto their host contigs. With the current focus on sequencing GRCs from different songbirds, our work adds to the genomic toolkit to identify GRC elements, and we provide a detailed protocol and GitHub repository at https://github.com/brachtlab/Comparative_Coverage_Analysis (last accessed May 12, 2021)., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.)

High levels of heterozygosity present a unique genome assembly challenge and can adversely impact downstream analyses, yet is common in sequencing datasets obtained from non-model organisms. Here we show that by re-assembling a heterozygous dataset with variant parameters and different assembly algorithms, we are able to generate assemblies whose protein annotations are statistically enriched for specific gene ontology categories. While total assembly length was not significantly affected by assembly methodologies tested, the assemblies generated varied widely in fragmentation level and we show local assembly collapse or expansion underlying the enrichment or depletion of specific protein functional groups. We show that these statistically significant deviations in gene ontology groups can occur in seemingly high-quality assemblies, and result from difficult-to-detect local sequence expansion or contractions. Given the unpredictable interplay between assembly algorithm, parameter, and biological sequence data heterozygosity, we highlight the need for better measures of assembly quality than N50 value, including methods for assessing local expansion and collapse., Competing Interests: The authors have declared that no competing interests exist.

The nematode Halicephalobus mephisto was originally discovered inhabiting a deep terrestrial aquifer 1.3 km underground. H. mephisto can thrive under conditions of abiotic stress including heat and minimal oxygen, where it feeds on a community of both chemolithotrophic and heterotrophic prokaryotes in an unusual ecosystem isolated from the surface biosphere. Here we report the comprehensive genome and transcriptome of this organism, identifying a signature of adaptation: an expanded repertoire of 70 kilodalton heat-shock proteins (Hsp70) and avrRpt2 induced gene 1 (AIG1) proteins. The expanded Hsp70 genes are transcriptionally induced upon growth under heat stress, and we find that positive selection is detectable in several members of this family. We further show that AIG1 may have been acquired by horizontal gene transfer (HGT) from a rhizobial fungus. Over one-third of the genes of H. mephisto are novel, highlighting the divergence of this nematode from other sequenced organisms. This work sheds light on the genomic basis of heat tolerance in a complete subterrestrial eukaryotic genome.

Subtractive genomics can be used in any research where the goal is to identify the sequence of a gene, protein, or general region that is embedded in a larger genomic context. Subtractive genomics enables a researcher to isolate a target sequence of interest (T) by comprehensive sequencing and subtracting out known genetic elements (reference, R). The method can be used to identify novel sequences such as mitochondria, chloroplasts, viruses, or germline restricted chromosomes, and is particularly useful when T cannot be easily isolated from R. Beginning with the comprehensive genomic data (R + T), the method uses Basic Local Alignment Search Tool (BLAST) against a reference sequence, or sequences, to remove the matching known sequences (R), leaving behind the target (T). For subtraction to work best, R should be a relatively complete draft that is missing T. Since sequences remaining after subtraction are tested through quantitative Polymerase Chain Reaction (qPCR), R does not need to be complete for the method to work. Here we link computational steps with experimental steps into a cycle that can be iterated as needed, sequentially removing multiple reference sequences and refining the search for T. The advantage of subtractive genomics is that a completely novel target sequence can be identified even in cases in which physical purification is difficult, impossible, or expensive. A drawback of the method is finding a suitable reference for subtraction and obtaining T-positive and negative samples for qPCR testing. We describe our implementation of the method in the identification of the first gene from the germline-restricted chromosome of zebra finch. In that case computational filtering involved three references (R), sequentially removed over three cycles: an incomplete genomic assembly, raw genomic data, and transcriptomic data.

Developmentally programmed genome rearrangements are rare in vertebrates, but have been reported in scattered lineages including the bandicoot, hagfish, lamprey, and zebra finch (Taeniopygia guttata) [1]. In the finch, a well-studied animal model for neuroendocrinology and vocal learning [2], one such programmed genome rearrangement involves a germline-restricted chromosome, or GRC, which is found in germlines of both sexes but eliminated from mature sperm [3, 4]. Transmitted only through the oocyte, it displays uniparental female-driven inheritance, and early in embryonic development is apparently eliminated from all somatic tissue in both sexes [3, 4]. The GRC comprises the longest finch chromosome at over 120 million base pairs [3], and previously the only known GRC-derived sequence was repetitive and non-coding [5]. Because the zebra finch genome project was sourced from male muscle (somatic) tissue [6], the remaining genomic sequence and protein-coding content of the GRC remain unknown. Here we report the first protein-coding gene from the GRC: a member of the α-soluble N-ethylmaleimide sensitive fusion protein (NSF) attachment protein (α-SNAP) family hitherto missing from zebra finch gene annotations. In addition to the GRC-encoded α-SNAP, we find an additional paralogous α-SNAP residing in the somatic genome (a somatolog)-making the zebra finch the first example in which α-SNAP is not a single-copy gene. We show divergent, sex-biased expression for the paralogs and also that positive selection is detectable across the bird α-SNAP lineage, including the GRC-encoded α-SNAP. This study presents the identification and evolutionary characterization of the first protein-coding GRC gene in any organism., (Copyright © 2018 Elsevier Ltd. All rights reserved.)