Automated cellular annotation for high-resolution images of adult Caenorhabditis elegans.

MOTIVATION: Advances in high-resolution microscopy have recently made possible the analysis of gene expression at the level of individual cells. The fixed lineage of cells in the adult worm Caenorhabditis elegans makes this organism an ideal model for studying complex biological processes like development and aging. However, annotating individual cells in images of adult C.elegans typically requires expertise and significant manual effort. Automation of this task is therefore critical to enabling high-resolution studies of a large number of genes. RESULTS: In this article, we describe an automated method for annotating a subset of 154 cells (including various muscle, intestinal and hypodermal cells) in high-resolution images of adult C.elegans. We formulate the task of labeling cells within an image as a combinatorial optimization problem, where the goal is to minimize a scoring function that compares cells in a test input image with cells from a training atlas of manually annotated worms according to various spatial and morphological characteristics. We propose an approach for solving this problem based on reduction to minimum-cost maximum-flow and apply a cross-entropy-based learning algorithm to tune the weights of our scoring function. We achieve 84% median accuracy across a set of 154 cell labels in this highly variable system. These results demonstrate the feasibility of the automatic annotation of microscopy-based images in adult C.elegans.

Reconstructing cancer genomes from paired-end sequencing data.

BACKGROUND: A cancer genome is derived from the germline genome through a series of somatic mutations. Somatic structural variants - including duplications, deletions, inversions, translocations, and other rearrangements - result in a cancer genome that is a scrambling of intervals, or "blocks" of the germline genome sequence. We present an efficient algorithm for reconstructing the block organization of a cancer genome from paired-end DNA sequencing data. RESULTS: By aligning paired reads from a cancer genome - and a matched germline genome, if available - to the human reference genome, we derive: (i) a partition of the reference genome into intervals; (ii) adjacencies between these intervals in the cancer genome; (iii) an estimated copy number for each interval. We formulate the Copy Number and Adjacency Genome Reconstruction Problem of determining the cancer genome as a sequence of the derived intervals that is consistent with the measured adjacencies and copy numbers. We design an efficient algorithm, called Paired-end Reconstruction of Genome Organization (PREGO), to solve this problem by reducing it to an optimization problem on an interval-adjacency graph constructed from the data. The solution to the optimization problem results in an Eulerian graph, containing an alternating Eulerian tour that corresponds to a cancer genome that is consistent with the sequencing data. We apply our algorithm to five ovarian cancer genomes that were sequenced as part of The Cancer Genome Atlas. We identify numerous rearrangements, or structural variants, in these genomes, analyze reciprocal vs. non-reciprocal rearrangements, and identify rearrangements consistent with known mechanisms of duplication such as tandem duplications and breakage/fusion/bridge (B/F/B) cycles. CONCLUSIONS: We demonstrate that PREGO efficiently identifies complex and biologically relevant rearrangements in cancer genome sequencing data. An implementation of the PREGO algorithm is available at http://compbio.cs.brown.edu/software/.

Reconstruction of genealogical relationships with applications to Phase III of HapMap.

MOTIVATION: Accurate inference of genealogical relationships between pairs of individuals is paramount in association studies, forensics and evolutionary analyses of wildlife populations. Current methods for relationship inference consider only a small set of close relationships and have limited to no power to distinguish between relationships with the same number of meioses separating the individuals under consideration (e.g. aunt-niece versus niece-aunt or first cousins versus great aunt-niece). RESULTS: We present CARROT (ClAssification of Relationships with ROTations), a novel framework for relationship inference that leverages linkage information to differentiate between rotated relationships, that is, between relationships with the same number of common ancestors and the same number of meioses separating the individuals under consideration. We demonstrate that CARROT clearly outperforms existing methods on simulated data. We also applied CARROT on four populations from Phase III of the HapMap Project and detected previously unreported pairs of third- and fourth-degree relatives. AVAILABILITY: Source code for CARROT is freely available at http://carrot.stanford.edu. CONTACT: sofiakp@stanford.edu.

Analysis of cell fate from single-cell gene expression profiles in C. elegans.

The C. elegans cell lineage provides a unique opportunity to look at how cell lineage affects patterns of gene expression. We developed an automatic cell lineage analyzer that converts high-resolution images of worms into a data table showing fluorescence expression with single-cell resolution. We generated expression profiles of 93 genes in 363 specific cells from L1 stage larvae and found that cells with identical fates can be formed by different gene regulatory pathways. Molecular signatures identified repeating cell fate modules within the cell lineage and enabled the generation of a molecular differentiation map that reveals points in the cell lineage when developmental fates of daughter cells begin to diverge. These results demonstrate insights that become possible using computational approaches to analyze quantitative expression from many genes in parallel using a digital gene expression atlas.

Computational identification of CDR3 sequence archetypes among immunoglobulin sequences in chronic lymphocytic leukemia.

The leukemia cells of unrelated patients with chronic lymphocytic leukemia (CLL) display a restricted repertoire of immunoglobulin (Ig) gene rearrangements with preferential usage of certain Ig gene segments. We developed a computational method to rigorously quantify biases in Ig sequence similarity in large patient databases and to identify groups of patients with unusual levels of sequence similarity. We applied our method to sequences from 1577 CLL patients through the CLL Research Consortium (CRC), and identified 67 similarity groups into which roughly 20% of all patients could be assigned. Immunoglobulin light chain class was highly correlated within all groups and light chain gene usage was similar within sets. Surprisingly, over 40% of the identified groups were composed of somatically mutated genes. This study significantly expands the evidence that antigen selection shapes the Ig repertoire in CLL.

A sequence-based survey of the complex structural organization of tumor genomes.

BACKGROUND: The genomes of many epithelial tumors exhibit extensive chromosomal rearrangements. All classes of genome rearrangements can be identified using end sequencing profiling, which relies on paired-end sequencing of cloned tumor genomes. RESULTS: In the present study brain, breast, ovary, and prostate tumors, along with three breast cancer cell lines, were surveyed using end sequencing profiling, yielding the largest available collection of sequence-ready tumor genome breakpoints and providing evidence that some rearrangements may be recurrent. Sequencing and fluorescence in situ hybridization confirmed translocations and complex tumor genome structures that include co-amplification and packaging of disparate genomic loci with associated molecular heterogeneity. Comparison of the tumor genomes suggests recurrent rearrangements. Some are likely to be novel structural polymorphisms, whereas others may be bona fide somatic rearrangements. A recurrent fusion transcript in breast tumors and a constitutional fusion transcript resulting from a segmental duplication were identified. Analysis of end sequences for single nucleotide polymorphisms revealed candidate somatic mutations and an elevated rate of novel single nucleotide polymorphisms in an ovarian tumor. CONCLUSION: These results suggest that the genomes of many epithelial tumors may be far more dynamic and complex than was previously appreciated and that genomic fusions, including fusion transcripts and proteins, may be common, possibly yielding tumor-specific biomarkers and therapeutic targets.