Andrew Zimmer

A scalable, fully automated process for construction of sequence-ready human exome targeted capture libraries

Genome targeting methods enable cost-effective capture of specific subsets of the genome for sequencing. We present here an automated, highly scalable method for carrying out the Solution Hybrid Selection capture approach that provides a dramatic increase in scale and throughput of sequence-ready libraries produced. Significant process improvements and a series of in-process quality control checkpoints are also added. These process improvements can also be used in a manual version of the protocol.

A scalable, fully automated process for construction of sequence-ready barcoded libraries for 454

We present an automated, high throughput library construction process for 454 technology. Sample handling errors and cross-contamination are minimized via end-to-end barcoding of plasticware, along with molecular DNA barcoding of constructs. Automation-friendly magnetic bead-based size selection and cleanup steps have been devised, eliminating major bottlenecks and significant sources of error. Using this methodology, one technician can create 96 sequence-ready 454 libraries in 2 days, a dramatic improvement over the standard method.

DNA sequence of human chromosome 17 and analysis of rearrangement in the human lineage

Chromosome 17 is unusual among the human chromosomes in many respects. It is the largest human autosome with orthology to only a single mouse chromosome, mapping entirely to the distal half of mouse chromosome 11. Chromosome 17 is rich in protein-coding genes, having the second highest gene density in the genome. It is also enriched in segmental duplications, ranking third in density among the autosomes. Here we report a finished sequence for human chromosome 17, as well as a structural comparison with the finished sequence for mouse chromosome 11, the first finished mouse chromosome. Comparison of the orthologous regions reveals striking differences. In contrast to the typical pattern seen in mammalian evolution, the human sequence has undergone extensive intrachromosomal rearrangement, whereas the mouse sequence has been remarkably stable. Moreover, although the human sequence has a high density of segmental duplication, the mouse sequence has a very low density. Notably, these segmental duplications correspond closely to the sites of structural rearrangement, demonstrating a link between duplication and rearrangement. Examination of the main classes of duplicated segments provides insight into the dynamics underlying expansion of chromosome-specific, low-copy repeats in the human genome.

An improved canine genome and a comprehensive catalogue of coding genes and non-coding transcripts.

The domestic dog, Canis familiaris, is a well-established model system for mapping trait and disease loci. While the original draft sequence was of good quality, gaps were abundant particularly in promoter regions of the genome, negatively impacting the annotation and study of candidate genes. Here, we present an improved genome build, canFam3.1, which includes 85 MB of novel sequence and now covers 99.8% of the euchromatic portion of the genome. We also present multiple RNA-Sequencing data sets from 10 different canine tissues to catalog ∼175,000 expressed loci. While about 90% of the coding genes previously annotated by EnsEMBL have measurable expression in at least one sample, the number of transcript isoforms detected by our data expands the EnsEMBL annotations by a factor of four. Syntenic comparison with the human genome revealed an additional ∼3,000 loci that are characterized as protein coding in human and were also expressed in the dog, suggesting that those were previously not annotated in the EnsEMBL canine gene set. In addition to ∼20,700 high-confidence protein coding loci, we found ∼4,600 antisense transcripts overlapping exons of protein coding genes, ∼7,200 intergenic multi-exon transcripts without coding potential, likely candidates for long intergenic non-coding RNAs (lincRNAs) and ∼11,000 transcripts were reported by two different library construction methods but did not fit any of the above categories. Of the lincRNAs, about 6,000 have no annotated orthologs in human or mouse. Functional analysis of two novel transcripts with shRNA in a mouse kidney cell line altered cell morphology and motility. All in all, we provide a much-improved annotation of the canine genome and suggest regulatory functions for several of the novel non-coding transcripts.

Analysis of the DNA sequence and duplication history of human chromosome 15

Here we present a finished sequence of human chromosome 15, together with a high-quality gene catalogue. As chromosome 15 is one of seven human chromosomes with a high rate of segmental duplication, we have carried out a detailed analysis of the duplication structure of the chromosome. Segmental duplications in chromosome 15 are largely clustered in two regions, on proximal and distal 15q; the proximal region is notable because recombination among the segmental duplications can result in deletions causing Prader-Willi and Angelman syndromes. Sequence analysis shows that the proximal and distal regions of 15q share extensive ancient similarity. Using a simple approach, we have been able to reconstruct many of the events by which the current duplication structure arose. We find that most of the intrachromosomal duplications seem to share a common ancestry. Finally, we demonstrate that some remaining gaps in the genome sequence are probably due to structural polymorphisms between haplotypes; this may explain a significant fraction of the gaps remaining in the human genome.

Human chromosome 11 DNA sequence and analysis including novel gene identification

Chromosome 11, although average in size, is one of the most gene- and disease-rich chromosomes in the human genome. Initial gene annotation indicates an average gene density of 11.6 genes per megabase, including 1,524 protein-coding genes, some of which were identified using novel methods, and 765 pseudogenes. One-quarter of the protein-coding genes shows overlap with other genes. Of the 856 olfactory receptor genes in the human genome, more than 40% are located in 28 single- and multi-gene clusters along this chromosome. Out of the 171 disorders currently attributed to the chromosome, 86 remain for which the underlying molecular basis is not yet known, including several mendelian traits, cancer and susceptibility loci. The high-quality data presented here—nearly 134.5 million base pairs representing 99.8% coverage of the euchromatic sequence—provide scientists with a solid foundation for understanding the genetic basis of these disorders and other biological phenomena.

GeNets: a unified web platform for network-based genomic analyses

Functional genomics networks are widely used to identify unexpected pathway relationships in large genomic datasets. However, it is challenging to compare the signal-to-noise ratios of different networks and to identify the optimal network with which to interpret a particular genetic dataset. We present GeNets, a platform in which users can train a machine-learning model (Quack) to carry out these comparisons and execute, store, and share analyses of genetic and RNA-sequencing datasets.The GeNets web platform can identify the most informative network, as well as execute, store and share network-based analyses of RNA-seq or genomic datasets.

A unified web platform for network-based analyses of genomic data

A major bottleneck in network-based analyses of genomic data is quantitatively comparing biological signal in different networks and to identifying the optimal network dataset to answer a particular biological question. Towards these aims, we developed a unified web platform 9Broad Institute Web Platform for Genome Networks (GeNets)9, where users can compare biological signal of networks, and execute, store, and share network analyses. We designed a machine learningmachine-learning algorithm (Quack) which), which uses topological features to can quantify the overall and pathway-specific biological signals in networks, thus enabling users to choose the optimal network dataset for their analyses. We illustrated a typical workflow using GeNets to identify interesting autism candidate genes in the network that, when compared to four other networks, best recapitulates established neurodevelopmental pathway information. GeNets is a scalable, general and uniquely enabling computational framework for analyzing, managing and sharing analyses of genetic datasets using heterogeneous functional genomics networks, for example, from single-cell transcriptional analyses.