Mito-seek enables deep analysis of mitochondrial DNA, revealing ubiquitous, stable heteroplasmy maintained by intercellular exchange

Ravi Sachidanandam, Anitha D Jayaprakash, Erica Benson, Raymond Liang, Jaehee Shim, Luca Lambertini, Mike Wigler, Stuart Aaronson
doi: http://dx.doi.org/10.1101/007005

Eukaryotic cells carry two genomes, nuclear (nDNA) and mitochondrial (mtDNA), which are ostensibly decoupled in their replication, segregation and inheritance. It is increasingly appreciated that heteroplasmy, the occurrence of multiple mtDNA haplotypes in a cell, plays an important biological role, but its features are not well understood. Until now, accurately determining the diversity of mtDNA has been difficult due to the relatively small amount of mtDNA in each cell ( 98%) mtDNA and its ability to detect rare variants is limited only by sequencing depth, providing unprecedented sensitivity and specificity. Using Mito-seek, we confirmed the ubiquity of heteroplasmy by analyzing mtDNA from a diverse set of cell lines and human samples. By applying Mito-seek to colonies derived from single cells, we showed that heteroplasmy is stably maintained in individual daughter cells over multiple cell divisions. Our simulations indicate that the stability of heteroplasmy can be facilitated by the exchange of mtDNA between cells. We also explicitly demonstrate this exchange by co-culturing cell lines with distinct mtDNA haplotypes. Our results shed new light on the maintenance of heteroplasmy and provide a novel platform to investigate various features of heteroplasmy in normal and diseased tissues.

Single haplotype assembly of the human genome from a hydatidiform mole

Karyn Meltz Steinberg, Valerie K Schneider, Tina A Graves-Lindsay, Robert S Fulton, Richa Agarwala, John Huddleston, Sergey A Shiryayev, Aleksandr Morgulis, Urvashi Surti, Wesley C Warren, Deanna M Church, Evan E Eichler, Richard K Wilson

An accurate and complete reference human genome sequence assembly is essential for accurately interpreting individual genomes and associating sequence variation with disease phenotypes. While the current reference genome sequence is of very high quality, gaps and misassemblies remain due to biological and technical complexities. Large repetitive sequences and complex allelic diversity are the two main drivers of assembly error. Although increasing the length of sequence reads and library fragments can help overcome these problems, even the longest available reads do not resolve all regions of the human genome. In order to overcome the issue of allelic diversity, we used genomic DNA from an essentially haploid hydatidiform mole, CHM1. We utilized several resources from this DNA including a set of end-sequenced and indexed BAC clones, an optical map, and 100X whole genome shotgun (WGS) sequence coverage using short (Illumina) read pairs. We used the WGS sequence and the GRCh37 reference assembly to create a sequence assembly of the CHM1 genome. We subsequently incorporated 382 finished CHORI-17 BAC clone sequences to generate a second draft assembly, CHM1_1.1 (NCBI AssemblyDB GCA_000306695.2). Analysis of gene and repeat content show this assembly to be of excellent quality and contiguity, and comparisons to ClinVar and the NHGRI GWAS catalog show that the CHM1 genome does not harbor an excess of deleterious alleles. However, comparison to assembly-independent resources, such as BAC clone end sequences and long reads generated by a different sequencing technology (PacBio), indicate misassembled regions. The great majority of these regions is enriched for structural variation and segmental duplication, and can be resolved in the future by sequencing BAC clone tiling paths. This publicly available first generation assembly will be integrated into the Genome Reference Consortium (GRC) curation framework for further improvement, with the ultimate goal being a completely finished gap-free assembly.

svaseq: removing batch effects and other unwanted noise from sequencing data

Jeffrey Leek

It is now well known that unwanted noise and unmodeled artifacts such as batch effects can dramatically reduce the accuracy of statistical inference in genomic experiments. We introduced surrogate variable analysis for estimating these artifacts by (1) identifying the part of the genomic data only affected by artifacts and (2) estimating the artifacts with principal components or singular vectors of the subset of the data matrix. The resulting estimates of artifacts can be used in subsequent analyses as adjustment factors. Here I describe an update to the sva approach that can be applied to analyze count data or FPKMs from sequencing experiments. I also describe the addition of supervised sva (ssva) for using control probes to identify the part of the genomic data only affected by artifacts. These updates are available through the surrogate variable analysis (sva) Bioconductor package.

Efficient Algorithms for de novo Assembly of Alternative Splicing Events from RNA-seq Data

Gustavo Sacomoto
(Submitted on 23 Jun 2014)

In this thesis, we address the problem of identifying and quantifying variants (alternative splicing and genomic polymorphism) in RNA-seq data when no reference genome is available, without assembling the full transcripts. Based on the fundamental idea that each variant corresponds to a recognizable pattern, a bubble, in a de Bruijn graph constructed from the RNA-seq reads, we propose a general model for all variants in such graphs. We then introduce an exact method, called KisSplice, to extract alternative splicing events. Finally, we show that it enables to identify more correct events than general purpose transcriptome assemblers.
In order to deal with ever-increasing volumes of NGS data, we put an extra effort to make KisSplice as scalable as possible. First, to improve its running time, we propose a new polynomial delay algorithm to enumerate bubbles. We show that it is several orders of magnitude faster than previous approaches. Then, to reduce its memory consumption, we propose a new compact way to build and represent a de Bruijn graph. We show that our approach uses 30% to 40% less memory than the state of the art, with an insignificant impact on the construction time.
Additionally, we apply the same techniques developed to list bubbles in two classical problems: cycle enumeration and the K-shortest paths problem. We give the first optimal algorithm to list cycles in undirected graphs, improving over Johnson’s algorithm. This is the first improvement to this problem in almost 40 years. We then consider a different parameterization of the classical K-shortest (simple) paths problem: instead of bounding the number of st-paths, we bound the weight of the st-paths. We present new algorithms with the same time complexities but using exponentially less memory than previous approaches.