Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments.Availability: http://samtools.sourceforge.netContact: rd@sanger.ac.uk

Microglia, the resident immune cells of the brain, rapidly change states in response to their environment, but we lack molecular and functional signatures of different microglial populations. Here, we analyzed the RNA expression patterns of more than 76,000 individual microglia in mice during development, in old age, and after brain injury. Our analysis uncovered at least nine transcriptionally distinct microglial states, which expressed unique sets of genes and were localized in the brain using specific markers. The greatest microglial heterogeneity was found at young ages; however, several statesincluding chemokine-enriched inflammatory microglia-persisted throughout the lifespan or increased in the aged brain. Multiple reactive microglial subtypes were also found following demyelinating injury in mice, at least one of which was also found in human multiple sclerosis lesions. These distinct microglia signatures can be used to better understand microglia function and to identify and manipulate specific subpopulations in health and disease.

The mammalian brain is composed of diverse, specialized cell populations. To systematically ascertain and learn from these cellular specializations, we used Drop-seq to profile RNA expression in 690,000 individual cells sampled from 9 regions of the adult mouse brain. We identified 565 transcriptionally distinct groups of cells using computational approaches developed to distinguish biological from technical signals. Cross-region analysis of these 565 cell populations revealed features of brain organization, including a gene-expression module for synthesizing axonal and presynaptic components, patterns in the co-deployment of voltage-gated ion channels, functional distinctions among the cells of the vasculature and specialization of glutamatergic neurons across cortical regions. Systematic neuronal classifications for two complex basal ganglia nuclei and the striatum revealed a rare population of spiny projection neurons. This adult mouse brain cell atlas, accessible through interactive online software (DropViz), serves as a reference for development, disease, and evolution.

Dissecting the genetic basis of disease risk requires measuring all forms of genetic variation, including SNPs and copy number variants (CNVs), and is enabled by accurate maps of their locations, frequencies and population-genetic properties. We designed a hybrid genotyping array (Affymetrix SNP 6.0) to simultaneously measure 906,600 SNPs and copy number at 1.8 million genomic locations. By characterizing 270 HapMap samples, we developed a map of human CNV (at 2-kb breakpoint resolution) informed by integer genotypes for 1,320 copy number polymorphisms (CNPs) that segregate at an allele frequency>1%. More than 80% of the sequence in previously reported CNV regions fell outside our estimated CNV boundaries, indicating that large (>100 kb) CNVs affect much less of the genome than initially reported. Approximately 80% of observed copy number differences between pairs of individuals were due to common CNPs with an allele frequency >5%, and more than 99% derived from inheritance rather than new mutation. Most common, diallelic CNPs were in strong linkage disequilibrium with SNPs, and most low-frequency CNVs segregated on specific SNP haplotypes.

Accurate and complete measurement of single nucleotide (SNP) and copy number (CNV) variants, both common and rare, will be required to understand the role of genetic variation in disease. We present Birdsuite, a four-stage analytical framework instantiated in software for deriving integrated and mutually consistent copy number and SNP genotypes. The method sequentially assigns copy number across regions of common copy number polymorphisms (CNPs), calls genotypes of SNPs, identifies rare CNVs via a hidden Markov model (HMM), and generates an integrated sequence and copy number genotype at every locus (for example, including genotypes such as A-null, AAB and URLs. Birdsuite, http://www.broad.mit.edu/mpg/birdsuite/; PLINK CNV analysis tools, http://pngu.mgh.harvard.edu/purcell/plink/cnv; 1000 Genomes, http://www.1000genomes.org. COMPETING INTERESTS STATEMENTThe authors declare competing financial interests: details accompany the full-text HTML version of the paper at http://www.nature.com/naturegenetics/. BBB in addition to AA, AB and BB calls). Such genotypes more accurately depict the underlying sequence of each individual, reducing the rate of apparent mendelian inconsistencies. The Birdsuite software is applied here to data from the Affymetrix SNP 6.0 array. Additionally, we describe a method, implemented in PLINK, to utilize these combined SNP and CNV genotypes for association testing with a phenotype. NIH Public AccessStudies of SNPs and CNVs in human disease have to date been built on different analytical approaches, and somewhat based on conflicting assumptions. Specifically, SNP genotyping methods 1,2 assume that every individual has two copies of each locus, whereas studies of copy number variation assume that individuals vary in their copy number across the genome. Because SNPs and CNVs coexist throughout the genome, they influence one another's measurement, and may act both separately and in concert to influence human phenotypes. Ignoring CNVs during SNP genotyping results in failure to capture the true underlying sequence at many sites (genotypes like AAB and A), and can create the appearance of violations of mendelian inheritance or Hardy-Weinberg equilibrium where none in fact exists 3,4 . Ignoring SNPs in copy number analysis fails to incorporate allele-specific gains and losses, as well as the potential to exploit linkage disequilibrium between CNVs and nearby SNPs.In addition, methods for copy number analysis have not previously separated the ideas of genotyping known copy number polymorphisms (CNPs) from discovery of rare (and thus previously unobserved) copy number variants (CNVs) 5 . In the former case, as in SNP genotyping, existing information about known polymorphisms can be used to design arrays, train clustering algorithms and assign a prior probability of aberrant copy number to guide interpretation of measurements. Discovery of rare variants, as in sequence analysis for rare mutations, is a much more difficult problem-both because it is more difficult to detect a single event tha...

RNA-Seq is an effective method to study the transcriptome, but can be difficult to apply to scarce or degraded RNA from fixed clinical samples, rare cell populations, or cadavers. Recent studies have proposed several methods for RNA-Seq of low quality and/or low quantity samples, but their relative merits have not been systematically analyzed. Here, we compare five such methods using metrics relevant to transcriptome annotation, transcript discovery, and gene expression. Using a single human RNA sample, we constructed and sequenced ten libraries with these methods and two control libraries. We find that the RNase H method performed best for low quality RNA, and confirmed this with actual degraded samples. RNase H can even effectively replace oligo (dT) based methods for standard RNA-Seq. SMART and NuGEN had distinct strengths for low quantity RNA. Our analysis allows biologists to select the most suitable methods and provides a benchmark for future method development.

Primates and rodents, which descended from a common ancestor around 90 million years ago 1 , exhibit profound differences in behaviour and cognitive capacity; the cellular basis for these differences is unknown. Here we use single-nucleus RNA sequencing to profile RNA expression in 188,776 individual interneurons across homologous brain regions from three primates (human, macaque and marmoset), a rodent (mouse) and a weasel (ferret). Homologous interneuron types-which were readily identified by their RNA-expression patterns-varied in abundance and RNA expression among ferrets, mice and primates, but varied less among primates. Only a modest fraction of the genes identified as 'markers' of specific interneuron subtypes in any one species had this property in another species. In the primate neocortex, dozens of genes showed spatial expression gradients among interneurons of the same type, which suggests that regional variation in cortical contexts shapes the RNA expression patterns of adult neocortical interneurons. We found that an interneuron type that was previously associated with the mouse hippocampus-the 'ivy cell', which has neurogliaform characteristics-has become abundant across the neocortex of humans, macaques and marmosets but not mice or ferrets. We also found a notable subcortical innovation: an abundant striatal interneuron type in primates that had no molecularly homologous counterpart in mice or ferrets. These interneurons expressed a unique combination of genes that encode transcription factors, receptors and neuropeptides and constituted around 30% of striatal interneurons in marmosets and humans.Vertebrate brains contain many specialized brain structures, each with its own evolutionary history. For example, the six-layer neocortex arose in mammals about 200 million years ago 2 , whereas distinct basal ganglia were already present in the last common ancestor of vertebrates more than 500 million years ago 3 .Brain evolution may often be driven by adaptive changes in cellular composition or molecular expression within conserved structures [4][5][6] . Examples of modifications to specific cell types within larger conserved brain systems include hindbrain circuits that control species-specific courtship calls in frogs 7 , the evolution of trichromatic vision in primates 8 , and neurons that have converted from motor to sensory processing to produce a new swimming behaviour in sand crabs 4 . Evolution can modify brain structures through diverse means, such as by increasing or reducing the production or survival of cells of a given type, altering the molecular and cellular properties of shared cell types, reallocating or redeploying cell types to new locations, losing a cell type 9 or inventing a new cell type (Fig. 1a).Genome and RNA sequencing (RNA-seq) analyses have allowed molecular inventories to be compared across species 10,11 , and single-cell RNA-seq now enables the detailed comparison of cell types and expression patterns between homologous brain structures 8,10,11 . A recent study compared n...

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