Marlon Stoeckius, Christoph Hafemeister, William Stephenson, Brian Houck-Loomis, Pratip K Chattopadhyay, Harold Swerdlow, Rahul Satija, Peter Smibert

Recent high-throughput single-cell sequencing approaches have been transformative for understanding complex cell populations, but are unable to provide additional phenotypic information, such as protein levels of cell-surface markers. Using oligonucleotide-labeled antibodies, we integrate measurements of cellular proteins and transcriptomes into an efficient, sequencing-based readout of single cells. This method is compatible with existing single-cell sequencing approaches and will readily scale as the throughput of these methods increase.

Sissel Juul, Fernando Izquierdo, Adam Hurst, Xiaoguang Dai, Amber Wright, Eugene Kulesha, Roger Pettett, Daniel J Turner

Whole genome sequencing on next-generation instruments provides an unbiased way to identify the organisms present in complex metagenomic samples. However, the time-to-result can be protracted because of fixed-time sequencing runs and cumbersome bioinformatics workflows. This limits the utility of the approach in settings where rapid species identification is crucial, such as in the quality control of food-chain components, or in during an outbreak of an infectious disease. Here we present What′s in my Pot? (WIMP), a laboratory and analysis workflow in which, starting with an unprocessed sample, sequence data is generated and bacteria, viruses and fungi present in the sample are classified to subspecies and strain level in a quantitative manner, without prior knowledge of the sample composition, in approximately 3.5 hours. This workflow relies on the combination of Oxford Nanopore Technologies′ MinION ™ sensing device with a real-time species identification bioinformatics application.

Elphege P Nora, Anton Goloborodko, Anne-Laure Valton, Johan Harmen Gibcus, Alec Uebersohn, Nezar Abdennur, Job Dekker, Leonid A. Mirny, Benoit Bruneau

The molecular mechanisms underlying folding of mammalian chromosomes remain poorly understood. The transcription factor CTCF is a candidate regulator of chromosomal structure. Using the auxin-inducible degron system in mouse embryonic stem cells, we show that CTCF is absolutely and dose-dependently required for looping between CTCF target sites and segmental organization into topologically associating domains (TADs). Restoring CTCF reinstates proper architecture on altered chromosomes, indicating a powerful instructive function for CTCF in chromatin folding, and CTCF remains essential for TAD organization in non-dividing cells. Surprisingly, active and inactive genome compartments remain properly segregated upon CTCF depletion, revealing that compartmentalization of mammalian chromosomes emerges independently of proper insulation of TADs. Further, our data supports that CTCF mediates transcriptional insulator function through enhancer-blocking but not direct chromatin barrier activity. These results define the functions of CTCF in chromosome folding, and provide new fundamental insights into the rules governing mammalian genome organization.

Chuan-Chao Wang, Sabine Reinhold, Alexey Kalmykov, Antje Wissgott, Guido Brandt, Choongwon Jeong, Olivia Cheronet, Matthew Ferry, Eadaoin Harney, Denise Keating, Swapan Mallick, Nadin Rohland, Kristin Stewardson, Anatoly R. Kantorovich, Vladimir E. Maslov, Vladimira G. Petrenko, Vladimir R. Erlikh, Biaslan Ch. Atabiev, Rabadan G. Magomedov, Philipp L. Kohl, Kurt W. Alt, Sandra L. Pichler, Claudia Gerling, Harald Meller, Benik Vardanyan, Larisa Yeganyan, Alexey D. Rezepkin, Dirk Mariaschk, Natalia Berezina, Julia Gresky, Katharina Fuchs, Corina Knipper, Stephan Schiffels, Elena Balanovska, Oleg Balanovsky, Iain Mathieson, Thomas Higham, Yakov B. Berezin, Alexandra Buzhilova, Viktor Trifonov, Ron Pinhasi, Andrej B. Belinskiy, David Reich, Svend Hansen, Johannes Krause, Wolfgang Haak

Archaeogenetic studies have described the formation of Eurasian 'steppe ancestry' as a mixture of Eastern and Caucasus hunter-gatherers. However, it remains unclear when and where this ancestry arose and whether it was related to a horizon of cultural innovations in the 4th millennium BCE that subsequently facilitated the advance of pastoral societies likely linked to the dispersal of Indo-European languages. To address this, we generated genome-wide SNP data from 45 prehistoric individuals along a 3000-year temporal transect in the North Caucasus. We observe a genetic separation between the groups of the Caucasus and those of the adjacent steppe. The Caucasus groups are genetically similar to contemporaneous populations south of it, suggesting that - unlike today - the Caucasus acted as a bridge rather than an insurmountable barrier to human movement. The steppe groups from Yamnaya and subsequent pastoralist cultures show evidence for previously undetected Anatolian farmer-related ancestry from different contact zones, while Steppe Maykop individuals harbour additional Upper Palaeolithic Siberian and Native American related ancestry.

Matthew Loose, Sunir Malla, Michael Stout

The Oxford Nanopore MinION is a portable real time sequencing device which functions by sensing the change in current flow through a nanopore as DNA passes through it. These current values can be streamed in real time from individual nanopores as DNA molecules traverse them. Furthermore, the technology enables individual DNA molecules to be rejected on demand by reversing the voltage across specific channels. In theory, combining these features enables selection of individual DNA molecules for sequencing from a pool, an approach called "Read Until". Here we apply dynamic time warping to match short query current traces to references, demonstrating selection of specific regions of small genomes, individual amplicons from a group of targets, or normalisation of amplicons in a set. This is the first demonstration of direct selection of specific DNA molecules in real time whilst sequencing on any device and enables many novel uses for the MinION.

Amalio Telenti, Levi C.T. Pierce, William H. Biggs, Julia di Iulio, Emily H.M. Wong, Martin M Fabani, Ewen F. Kirkness, Ahmed Moustafa, Naisha Shah, Chao Xie, Suzanne C Brewerton, Nadeem Bulsara, Chad Garner, Gary Metzker, Efren Sandoval, Brad A Perkins, Franz J Och, Yaron Turpaz, J. Craig Venter

We report on the sequencing of 10,545 human genomes at 30-40x coverage with an emphasis on quality metrics and novel variant and sequence discovery. We find that 84% of an individual human genome can be sequenced confidently. This high confidence region includes 91.5% of exon sequence and 95.2% of known pathogenic variant positions. We present the distribution of over 150 million single nucleotide variants in the coding and non-coding genome. Each newly sequenced genome contributes an average of 8,579 novel variants. In addition, each genome carries in average 0.7 Mb of sequence that is not found in the main build of the hg38 reference genome. The density of this catalog of variation allowed us to construct high resolution profiles that define genomic sites that are highly intolerant of genetic variation. These results indicate that the data generated by deep genome sequencing is of the quality necessary for clinical use.

Jase Gehring, Jong Hwee Park, Sisi Chen, Matthew Thomson, Lior Pachter

We describe a universal sample multiplexing method for single-cell RNA-seq in which cells are chemically labeled with identifying DNA oligonucleotides. Analysis of a 96-plex perturbation experiment revealed changes in cell population structure and transcriptional states that cannot be discerned from bulk measurements, establishing a cost effective means to survey cell populations from large experiments and clinical samples with the depth and resolution of single-cell RNA-seq.

Mahul Chakraborty, James G. Baldwin-Brown, Anthony D. Long, J.J. Emerson

Genome assemblies that are accurate, complete, and contiguous are essential for identifying important structural and functional elements of genomes and for identifying genetic variation. Nevertheless, most recent genome assemblies remain incomplete and fragmented. While long molecule sequencing promises to deliver more complete genome assemblies with fewer gaps, concerns about error rates, low yields, stringent DNA requirements, and uncertainty about best practices may discourage many investigators from adopting this technology. Here, in conjunction with the platinum standard Drosophila melanogaster reference genome, we analyze recently published long molecule sequencing data to identify what governs completeness and contiguity of genome assemblies. We also present a hybrid meta-assembly approach that achieves remarkable assembly contiguity for both Drosophila and human assemblies with only modest long molecule sequencing coverage. Our results motivate a set of preliminary best practices for obtaining accurate and contiguous assemblies, a ″missing manual″ that guides key decisions in building high quality de novo genome assemblies, from DNA isolation to polishing the assembly.

Aravind Subramanian, Rajiv Narayan, Steven M. Corsello, David D. Peck, Ted E. Natoli, Xiaodong Lu, Joshua Gould, John F. Davis, Andrew A. Tubelli, Jacob K. Asiedu, David L. Lahr, Jodi E. Hirschman, Zihan Liu, Melanie Donahue, Bina Julian, Mariya Khan, David Wadden, Ian Smith, Daniel Lam, Arthur Liberzon, Courtney Toder, Mukta Bagul, Marek Orzechowski, Oana M Enache, Federica Piccioni, Alice H. Berger, Alykhan Shamji, Angela N Brooks, Anita Vrcic, Corey Flynn, Jacqueline Rosains, David Takeda, Desiree Davison, Justin Lamb, Kristin Ardlie, Larson Hogstrom, Nathanael S. Gray, Paul A Clemons, Serena Silver, Xiaoyun Wu, Wen-Ning Zhao, Willis Read-Button, Xiaohua Wu, Stephen J Haggarty, Lucienne V. Ronco, Jesse S Boehm, Stuart L Schreiber, John G. Doench, Joshua A. Bittker, David E Root, Bang Wong, Todd R. Golub

We previously piloted the concept of a Connectivity Map (CMap), whereby genes, drugs and disease states are connected by virtue of common gene-expression signatures. Here, we report more than a 1,000-fold scale-up of the CMap as part of the NIH LINCS Consortium, made possible by a new, low-cost, high throughput reduced representation expression profiling method that we term L1000. We show that L1000 is highly reproducible, comparable to RNA sequencing, and suitable for computational inference of the expression levels of 81% of non-measured transcripts. We further show that the expanded CMap can be used to discover mechanism of action of small molecules, functionally annotate genetic variants of disease genes, and inform clinical trials. The 1.3 million L1000 profiles described here, as well as tools for their analysis, are available at https://clue.io.

Ansuman T. Satpathy, Jeffrey M. Granja, Kathryn E Yost, Yanyan Qi, Francesca Meschi, Geoffrey P McDermott, Brett N Olsen, Maxwell R. Mumbach, Sarah E Pierce, M. Ryan Corces, Preyas Shah, Jason C. Bell, Darisha Jhutty, Corey M Nemec, Jean Wang, Li Wang, Yifeng Yin, Paul G Giresi, Anne Lynn S. Chang, Grace X.Y. Zheng, William J. Greenleaf, Howard Y. Chang

Understanding complex tissues requires single-cell deconstruction of gene regulation with precision and scale. Here we present a massively parallel droplet-based platform for mapping transposase-accessible chromatin in tens of thousands of single cells per sample (scATAC-seq). We obtain and analyze chromatin profiles of over 200,000 single cells in two primary human systems. In blood, scATAC-seq allows marker-free identification of cell type-specific cis- and trans-regulatory elements, mapping of disease-associated enhancer activity, and reconstruction of trajectories of differentiation from progenitors to diverse and rare immune cell types. In basal cell carcinoma, scATAC-seq reveals regulatory landscapes of malignant, stromal, and immune cell types in the tumor microenvironment. Moreover, scATAC-seq of serial tumor biopsies before and after PD-1 blockade allows identification of chromatin regulators and differentiation trajectories of therapy-responsive intratumoral T cell subsets, revealing a shared regulatory program driving CD8+ T cell exhaustion and CD4+ T follicular helper cell development. We anticipate that droplet-based single-cell chromatin accessibility will provide a broadly applicable means of identifying regulatory factors and elements that underlie cell type and function.

Mark J.P. Chaisson, Ashley D. Sanders, Xuefang Zhao, Ankit Malhotra, David Porubsky, Tobias Rausch, Eugene J. Gardner, Oscar Rodriguez, Li Guo, Ryan L Collins, Xian Fan, Jia Wen, Robert E Handsaker, Susan Fairley, Zev N. Kronenberg, Xiangmeng Kong, Fereydoun Hormozdiari, Dillon Lee, Aaron M Wenger, Alex Hastie, Danny Antaki, Peter Audano, Harrison Brand, Stuart Cantsilieris, Han Cao, Eliza Cerveira, Chong Chen, Xintong Chen, Chen-Shan Chin, Zechen Chong, Nelson T. Chuang, Christine C. Lambert, Deanna M Church, Laura Clarke, Andrew Farrell, Joey Flores, Timur Galeev, David Gorkin, Madhusudan Gujral, Victor Guryev, William Haynes Heaton, Jonas Korlach, Sushant Kumar, Jee Young Kwon, Jong Eun Lee, Joyce Lee, Wan-Ping Lee, Sau Peng Lee, Shantao Li, Patrick Marks, Karine Viaud-Martinez, Sascha Meiers, Katherine M. Munson, Fabio Navarro, Bradley J Nelson, Conor Nodzak, Amina Noor, Sofia Kyriazopoulou-Panagiotopoulou, Andy Pang, Yunjiang Qiu, Gabriel Rosanio, Mallory Ryan, Adrian Stütz, Diana C.J. Spierings, Alistair Ward, AnneMarie E. Welch, Ming Xiao, Wei Xu, Chengsheng Zhang, Qihui Zhu, Xiangqun Zheng-Bradley, Ernesto Lowy, Sergei Yakneen, Steven McCarroll, Goo Jun, Li Ding, Chong Lek Koh, Bing Ren, Paul Flicek, Ken Chen, Mark B Gerstein, Pui-Yan Kwok, Peter M. Lansdorp, Gabor Marth, Jonathan Sebat, Xinghua Shi, Ali Bashir, Kai Ye, Scott E. Devine, Michael Talkowski, Ryan E. Mills, Tobias Marschall, Jan O Korbel, Evan E Eichler, Charles Lee

The incomplete identification of structural variants (SVs) from whole-genome sequencing data limits studies of human genetic diversity and disease association. Here, we apply a suite of long-read, short-read, and strand-specific sequencing technologies, optical mapping, and variant discovery algorithms to comprehensively analyze three human parent-child trios to define the full spectrum of human genetic variation in a haplotype-resolved manner. We identify 818,054 indel variants (<50 bp) and 27,622 SVs (≥50 bp) per human genome. We also discover 156 inversions per genome - most of which previously escaped detection. Fifty-eight of the inversions we discovered intersect with the critical regions of recurrent microdeletion and microduplication syndromes. Taken together, our SV callsets represent a sevenfold increase in SV detection compared to most standard high-throughput sequencing studies, including those from the 1000 Genomes Project. The method and the dataset serve as a gold standard for the scientific community and we make specific recommendations for maximizing structural variation sensitivity for future large-scale genome sequencing studies.

Stephane E Castel, Ami Levy Moonshine, Pejman Mohammadi, Eric Banks, Tuuli Lappalainen

Allelic expression (AE) analysis has become an important tool for integrating genome and transcriptome data to characterize various biological phenomena such as cis-regulatory variation and nonsense-mediated decay. In this paper, we systematically analyze the properties of AE read count data and technical sources of error, such as low-quality or double-counted RNA-seq reads, genotyping errors, allelic mapping bias, and technical covariates due to sample preparation and sequencing, and variation in total read depth. We provide guidelines for correcting and filtering for such errors, and show that the resulting AE data has extremely low technical noise. Finally, we introduce novel software for high-throughput production of AE data from RNA-sequencing data, implemented in the GATK framework. These improved tools and best practices for AE analysis yield higher quality AE data by reducing technical bias. This provides a practical framework for wider adoption of AE analysis by the genomics community.

Elisabetta Mereu, Atefeh Lafzi, Catia Moutinho, Christoph Ziegenhain, Davis J. MacCarthy, Adrian Alvarez, Eduard Batlle, Sagar, Dominic Grün, Julia K. Lau, Stéphane C Boutet, Chad Sanada, Aik Ooi, Robert C. Jones, Kelly Kaihara, Chris Brampton, Yasha Talaga, Yohei Sasagawa, Kaori Tanaka, Tetsutaro Hayashi, Itoshi Nikaido, Cornelius Fischer, Sascha Sauer, Timo Trefzer, Christian Conrad, Xian Adiconis, Lan T. Nguyen, Aviv Regev, Joshua Z Levin, Swati Parekh, Aleksandar Janjic, Lucas E. Wange, Johannes W. Bagnoli, Wolfgang Enard, Ivo G Gut, Rickard Sandberg, Ivo Gut, Oliver Stegle, Holger Heyn

Single-cell RNA sequencing (scRNA-seq) is the leading technique for charting the molecular properties of individual cells. The latest methods are scalable to thousands of cells, enabling in-depth characterization of sample composition without prior knowledge. However, there are important differences between scRNA-seq techniques, and it remains unclear which are the most suitable protocols for drawing cell atlases of tissues, organs and organisms. We have generated benchmark datasets to systematically evaluate techniques in terms of their power to comprehensively describe cell types and states. We performed a multi-center study comparing 13 commonly used single-cell and single-nucleus RNA-seq protocols using a highly heterogeneous reference sample resource. Comparative and integrative analysis at cell type and state level revealed marked differences in protocol performance, highlighting a series of key features for cell atlas projects. These should be considered when defining guidelines and standards for international consortia, such as the Human Cell Atlas project.

Luyang Zhao, Liwei Deng, Gailing Li, Huan Jin, Jinsen Cai, Huan Shang, Yan Li, Haomin Wu, Weibin Xu, Lidong Zeng, Renli Zhang, Huan Zhao, Ping Wu, Zhiliang Zhou, Jiao Zheng, Pierre Ezanno, Qin Yan, Michael Deem, Jiankui He

Third generation sequencing is a direct measurement of DNA/RNA sequences at the single molecule level without amplification. In this study, we report sequencing of the genome of the M13 virus by a new single molecule sequencing platform. Our platform detects single molecule fluorescence by the total internal reflection microscope technique, with sequencing-by-synthesis chemistry. We sequenced the genome of M13 to a depth of 316x and 100% coverage. The consensus sequence accuracy is 100%. We demonstrated that single molecule sequencing has no significant GC bias.

Peter J Skene, Steven Henikoff

Cleavage Under Targets and Release Using Nuclease (CUT&RUN) is an epigenomic profiling strategy in which antibody-targeted controlled cleavage by micrococcal nuclease releases specific protein-DNA complexes into the supernatant for paired-end DNA sequencing. As only the targeted fragments enter into solution, and the vast majority of DNA is left behind, CUT&RUN has exceptionally low background levels. CUT&RUN outperforms the most widely-used Chromatin Immunoprecipitation (ChIP) protocols in resolution, signal-to-noise, and depth of sequencing required. In contrast to ChIP, CUT&RUN is free of solubility and DNA accessibility artifacts and can be used to profile insoluble chromatin and to detect long-range 3D contacts without cross-linking. Here we present an improved CUT&RUN protocol that does not require isolation of nuclei and provides high-quality data starting with only 100 cells for a histone modification and 1000 cells for a transcription factor. From cells to purified DNA CUT&RUN requires less than a day at the lab bench.

Louis Lello, Steven G. Avery, Laurent Tellier, Ana I. Vazquez, Gustavo de los Campos, Stephen D. H. Hsu

We construct genomic predictors for heritable and extremely complex human quantitative traits (height, heel bone density, and educational attainment) using modern methods in high dimensional statistics (i.e., machine learning). Replication tests show that these predictors capture, respectively, ~40, 20, and 9 percent of total variance for the three traits. For example, predicted heights correlate ~0.65 with actual height; actual heights of most individuals in validation samples are within a few cm of the prediction. The variance captured for height is comparable to the estimated SNP heritability from GCTA (GREML) analysis, and seems to be close to its asymptotic value (i.e., as sample size goes to infinity), suggesting that we have captured most of the heritability for the SNPs used. Thus, our results resolve the common SNP portion of the "missing heritability" problem - i.e., the gap between prediction R-squared and SNP heritability. The ~20k activated SNPs in our height predictor reveal the genetic architecture of human height, at least for common SNPs. Our primary dataset is the UK Biobank cohort, comprised of almost 500k individual genotypes with multiple phenotypes. We also use other datasets and SNPs found in earlier GWAS for out-of-sample validation of our results.

Armin Raznahan, Neelroop Parikshak, Vijayendran Chandran, Jonathan Blumenthal, Liv Clasen, Aaron Alexander-Bloch, Andrew Zinn, Danny Wangsa, Jasen Wise, Declan Murphy, Patrick Bolton, Thomas Ried, Judith Ross, Jay Giedd, Daniel Geschwind

A fundamental question in the biology of sex-differences has eluded direct study in humans: how does sex chromosome dosage (SCD) shape genome function? To address this, we developed a systematic map of SCD effects on gene function by analyzing genome-wide expression data in humans with diverse sex chromosome aneuploidies (XO, XXX, XXY, XYY, XXYY). For sex chromosomes, we demonstrate a pattern of obligate dosage sensitivity amongst evolutionarily preserved X-Y homologs, and update prevailing theoretical models for SCD compensation by detecting X-linked genes whose expression increases with decreasing X- and/or Y-chromosome dosage. We further show that SCD-sensitive sex chromosome genes regulate specific co-expression networks of SCD-sensitive autosomal genes with critical cellular functions and a demonstrable potential to mediate previously documented SCD effects on disease. Our findings detail wide-ranging effects of SCD on genome function with implications for human phenotypic variation.

Cristopher V Van Hout, Ioanna Tachmazidou, Joshua D Backman, Joshua X Hoffman, Bin Ye, Ashutosh K Pandey, Claudia Gonzaga-Jauregui, Shareef Khalid, Daren Liu, Nilanjana Banerjee, Alexander H Li, O’Dushlaine Colm, Anthony Marcketta, Jeffrey Staples, Claudia Schurmann, Alicia Hawes, Evan Maxwell, Leland Barnard, Alexander Lopez, John Penn, Lukas Habegger, Andrew L Blumenfeld, Ashish Yadav, Kavita Praveen, Marcus Jones, William J Salerno, Wendy K Chung, Ida Surakka, Cristen J. Willer, Kristian Hveem, Joseph B Leader, David J Carey, David H Ledbetter, Geisinger-Regeneron DiscovEHR Collaboration, Lon Cardon, George D Yancopoulos, Aris Economides, Giovanni Coppola, Alan R Shuldiner, Suganthi Balasubramanian, Michael Cantor, Matthew R. Nelson, John Whittaker, Jeffrey G Reid, Jonathan Marchini, John D Overton, Robert A Scott, Gonçalo Abecasis, Laura Yerges-Armstrong, Aris Baras, on behalf of the Regeneron Genetics Center

The UK Biobank is a prospective study of 502,543 individuals, combining extensive phenotypic and genotypic data with streamlined access for researchers around the world. Here we describe the first tranche of large-scale exome sequence data for 49,960 study participants, revealing approximately 4 million coding variants (of which ~98.4% have frequency < 1%). The data includes 231,631 predicted loss of function variants, a >10-fold increase compared to imputed sequence for the same participants. Nearly all genes (>97%) had ≥1 predicted loss of function carrier, and most genes (>69%) had ≥10 loss of function carriers. We illustrate the power of characterizing loss of function variation in this large population through association analyses across 1,741 phenotypes. In addition to replicating a range of established associations, we discover novel loss of function variants with large effects on disease traits, including PIEZO1 on varicose veins, COL6A1 on corneal resistance, MEPE on bone density, and IQGAP2 and GMPR on blood cell traits. We further demonstrate the value of exome sequencing by surveying the prevalence of pathogenic variants of clinical significance in this population, finding that 2% of the population has a medically actionable variant. Additionally, we leverage the phenotypic data to characterize the relationship between rare BRCA1 and BRCA2 pathogenic variants and cancer risk. Exomes from the first 49,960 participants are now made accessible to the scientific community and highlight the promise offered by genomic sequencing in large-scale population-based studies.

Shan Gao, Xiaoxuan Tian, Yu Sun, Zhenfeng Wu, Zhi Cheng, Pengzhi Dong, Qiang Zhao, Bingjun He, Jishou Ruan, Wenjun Bu

In this study, we introduced a general framework to use PacBio full-length transcriptome sequencing for the investigation of the fundamental problems in mitochondrial biology, e.g. genome arrangement, heteroplasmy, RNA processing and the regulation of transcription or replication. As a result, we produced the first full-length human mitochondrial transcriptome from the MCF7 cell line based on the PacBio platform and characterized the human mitochondrial transcriptome with more comprehensive and accurate information. The most important finding was two novel lnRNAs hsa-MDL1 and hsa-MDL1AS, which are encoded by the mitochondrial D-loop regions. We propose hsa-MDL1 and hsa-MDL1AS, as the precursors of transcription initiation RNAs (tiRNAs), belong to a novel class of long non-coding RNAs (lnRNAs), which is named as long tiRNAs (ltiRNAs). Based on the mitochondrial RNA processing model, the primary tiRNAs, precursors and mature tiRNAs could be discovered to completely reveal tiRNAs from their origins to functions. The MDL1 and MDL1AS lnRNAs and their regulation mechanisms exist ubiquitously from insects to human.

Gregory R Magoon, Raymond H Banks, Christian Rottensteiner, Bonnie E Schrack, Vincent O Tilroe, Terry Robb, Andrew J Grierson

An approach for generating high-resolution a priori maximum parsimony Y-chromosome (“chrY”) phylogenies based on SNP and small INDEL variant data from massively-parallel short-read (“next-generation”) sequencing data is described; the tree-generation methodology produces annotations localizing mutations to individual branches of the tree, along with indications of mutation placement uncertainty in cases for which "no-calls" (through lack of mapped reads or otherwise) at particular sites precludes precise phylogenetic placement of mutations. The approach leverages careful variant site filtering and a novel iterative reweighting procedure to generate high-accuracy trees while considering variants in regions of chrY that had previously been excluded from analyses based on short-read sequencing data. It is argued that the proposed approach is also superior to previous region-based filtering approaches in that it adapts to the quality of the underlying data and will automatically allow the scope of sites considered to expand as the underlying data quality improves (e.g. through longer read lengths). Key related issues, including calling of genotypes for the hemizygous chrY, reliability of variant results, read mismappings and "heterozygous" genotype calls, and the mutational stability of different variants are discussed and taken into account. The methodology is demonstrated through application to a dataset consisting of 1292 male samples from diverse populations and haplogroups, with the majority coming from low-coverage sequencing by the 1000 Genomes Project. Application of the tree-generation approach to these data produces a tree involving over 120,000 chrY variant sites (about 45,000 sites if “singletons” are excluded). The utility of this approach in refining the Y-chromosome phylogenetic tree is demonstrated by examining results for several haplogroups. The results indicate a number of new branches on the Y-chromosome phylogenetic tree, many of them subdividing known branches, but also including some that inform the presence of additional levels along the “trunk” of the tree. Finally, opportunities for extensions of this phylogenetic analysis approach to other types of genetic data are noted.