Widespread haploid-biased gene expression enables sperm-level natural selection.

Sperm are haploid but must be functionally equivalent to distribute alleles equally among progeny. Accordingly, gene products are shared through spermatid cytoplasmic bridges that erase phenotypic differences between individual haploid sperm. Here, we show that a large class of mammalian genes are not completely shared across these bridges. We call these genes "genoinformative markers" (GIMs) and show that a subset can act as selfish genetic elements that spread alleles unevenly through murine, bovine, and human populations. We identify evolutionary pressure to avoid conflict between sperm and somatic function as GIMs are enriched for testis-specific gene expression, paralogs, and isoforms. Therefore, GIMs and sperm-level natural selection may help to explain why testis gene expression patterns are an outlier relative to all other tissues.

Pan-cancer analysis reveals technical artifacts in TCGA germline variant calls.

BACKGROUND: Cancer research to date has largely focused on somatically acquired genetic aberrations. In contrast, the degree to which germline, or inherited, variation contributes to tumorigenesis remains unclear, possibly due to a lack of accessible germline variant data. Here we called germline variants on 9618 cases from The Cancer Genome Atlas (TCGA) database representing 31 cancer types. RESULTS: We identified batch effects affecting loss of function (LOF) variant calls that can be traced back to differences in the way the sequence data were generated both within and across cancer types. Overall, LOF indel calls were more sensitive to technical artifacts than LOF Single Nucleotide Variant (SNV) calls. In particular, whole genome amplification of DNA prior to sequencing led to an artificially increased burden of LOF indel calls, which confounded association analyses relating germline variants to tumor type despite stringent indel filtering strategies. The samples affected by these technical artifacts include all acute myeloid leukemia and practically all ovarian cancer samples. CONCLUSIONS: We demonstrate how technical artifacts induced by whole genome amplification of DNA can lead to false positive germline-tumor type associations and suggest TCGA whole genome amplified samples be used with caution. This study draws attention to the need to be sensitive to problems associated with a lack of uniformity in data generation in TCGA data.

Whole-genome mutational burden analysis of three pluripotency induction methods.

There is concern that the stresses of inducing pluripotency may lead to deleterious DNA mutations in induced pluripotent stem cell (iPSC) lines, which would compromise their use for cell therapies. Here we report comparative genomic analysis of nine isogenic iPSC lines generated using three reprogramming methods: integrating retroviral vectors, non-integrating Sendai virus and synthetic mRNAs. We used whole-genome sequencing and de novo genome mapping to identify single-nucleotide variants, insertions and deletions, and structural variants. Our results show a moderate number of variants in the iPSCs that were not evident in the parental fibroblasts, which may result from reprogramming. There were only small differences in the total numbers and types of variants among different reprogramming methods. Most importantly, a thorough genomic analysis showed that the variants were generally benign. We conclude that the process of reprogramming is unlikely to introduce variants that would make the cells inappropriate for therapy.

Using single nuclei for RNA-seq to capture the transcriptome of postmortem neurons.

A protocol is described for sequencing the transcriptome of a cell nucleus. Nuclei are isolated from specimens and sorted by FACS, cDNA libraries are constructed and RNA-seq is performed, followed by data analysis. Some steps follow published methods (Smart-seq2 for cDNA synthesis and Nextera XT barcoded library preparation) and are not described in detail here. Previous single-cell approaches for RNA-seq from tissues include cell dissociation using protease treatment at 30 °C, which is known to alter the transcriptome. We isolate nuclei at 4 °C from tissue homogenates, which cause minimal damage. Nuclear transcriptomes can be obtained from postmortem human brain tissue stored at -80 °C, making brain archives accessible for RNA-seq from individual neurons. The method also allows investigation of biological features unique to nuclei, such as enrichment of certain transcripts and precursors of some noncoding RNAs. By following this procedure, it takes about 4 d to construct cDNA libraries that are ready for sequencing.

Virmid: accurate detection of somatic mutations with sample impurity inference.

Detection of somatic variation using sequence from disease-control matched data sets is a critical first step. In many cases including cancer, however, it is hard to isolate pure disease tissue, and the impurity hinders accurate mutation analysis by disrupting overall allele frequencies. Here, we propose a new method, Virmid, that explicitly determines the level of impurity in the sample, and uses it for improved detection of somatic variation. Extensive tests on simulated and real sequencing data from breast cancer and hemimegalencephaly demonstrate the power of our model. A software implementation of our method is available at http://sourceforge.net/projects/virmid/.