GC Bias Research Articles

Abstract 2978: An integrated comparative analysis of TCGA lung adenocarcioma and lung squamous cell carcinoma copy number and RNA-Seq expression data

Abstract The Cancer Genome Atlas (TCGA) contains various types of genomic data from a wide variety of cancers, several of which affect the same tissue site. Here we analyze copy number and RNA-Seq data from TCGA for lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). Level 1 (raw) data, rather than Level 3 (segmented) data, from TCGA was used for an integrated analysis of copy number and gene expression profiles of the two cancers. Using the copy number and SNP- probes, re-processed tumor profiles were more consistent with a control set in terms of median number of copy number events, sample ploidy, and breakpoint genes than with the published “level 3” TCGA data. Probe-level data were analyzed using Nexus Copy Number SNP-FASST2 (a multi-state HMM algorithm that uses both SNP and copy number probes in making state assignments), with systematic correction applied to correct for GC biases. Additionally we performed manual baseline adjustment to correct for sample ploidy based on whole-genome B-alelle frequency data for each sample. Overall, the median number of copy number events in the LUAD TCGA data set was reduced from 371 (in the level 3 set) to 299, and from 681 (in the level 3 set) to 177 in the LUSC TCGA data set. After manual inspection, more than 38% of the TCGA LUAD samples and 50% of the TCGA LUSC samples available at level 3 were found to have incorrect baseline ploidy assignments. The resultant re-analyzed copy number data sets were used for an integrated analysis between the two tumor types. Comprehensive comparative analysis using Fisher's Exact Test revealed statistically significant differences (percent differential = 25%, p<0.001) in copy number profiles between the two lung tumor types; copy number changes include differential loss of chromosome 1p, loss of chromosome 3p, gain of chromosome 3q, loss of chromosome 4 and loss of chromosome 5. These correlated to changes in overall survival: individuals with loss of chromosome 1p and/or loss of 5q resulted in a significantly poorer prognosis (p<0.05). While more frequent in the LUSC sample population, this change in overall survival outcome was extended to samples with chromosome 1p loss in LUAD samples as well. Integration with RNA-Seq expression data from each tumor type revealed statistically significant correlations (p<0.05) with these copy number alterations, identifying potential driver genes of interest among each subtype and lung tumors in general. Citation Format: Andrea J. OHara, Raja Keshavan, Zhiwei Che, Soheil Shams. An integrated comparative analysis of TCGA lung adenocarcioma and lung squamous cell carcinoma copy number and RNA-Seq expression data. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 2978. doi:10.1158/1538-7445.AM2015-2978

Quantitative assessment of single-cell whole genome amplification methods for detecting copy number variation using hippocampal neurons.

Single-cell genomic analysis has grown rapidly in recent years and finds widespread applications in various fields of biology, including cancer biology, development, immunology, pre-implantation genetic diagnosis, and neurobiology. To date, the amplification bias, amplification uniformity and reproducibility of the three major single cell whole genome amplification methods (GenomePlex WGA4, MDA and MALBAC) have not been systematically investigated using mammalian cells. In this study, we amplified genomic DNA from individual hippocampal neurons using three single-cell DNA amplification methods, and sequenced them at shallow depth. We then systematically evaluated the GC-bias, reproducibility, and copy number variations among individual neurons. Our results showed that single-cell genome sequencing results obtained from the MALBAC and WGA4 methods are highly reproducible and have a high success rate. The MALBAC displays significant biases towards high GC content. We then attempted to correct the GC bias issue by developing a bioinformatics pipeline, which allows us to call CNVs in single cell sequencing data, and chromosome level and sub-chromosomal level CNVs among individual neurons can be detected. We also proposed a metric to determine the CNV detection limits. Overall, MALBAC and WGA4 have better performance than MDA in detecting CNVs.

Comparison of codon usage bias across Leishmania and Trypanosomatids to understand mRNA secondary structure, relative protein abundance and pathway functions

Understanding the variations in gene organization and its effect on the phenotype across different Leishmania species, and to study differential clinical manifestations of parasite within the host, we performed large scale analysis of codon usage patterns between Leishmania and other known Trypanosomatid species. We present the causes and consequences of codon usage bias in Leishmania genomes with respect to mutational pressure, translational selection and amino acid composition bias. We establish GC bias at wobble position that governs codon usage bias across Leishmania species, rather than amino acid composition bias. We found that, within Leishmania, homogenous codon context coding for less frequent amino acid pairs and codons avoiding formation of folding structures in mRNA are essentially chosen. We predicted putative differences in global expression between genes belonging to specific pathways across Leishmania. This explains the role of evolution in shaping the otherwise conserved genome to demonstrate species-specific function-level differences for efficient survival.

Strong spurious transcription likely contributes to DNA insert bias in typical metagenomic clone libraries.

BackgroundClone libraries provide researchers with a powerful resource to study nucleic acid from diverse sources. Metagenomic clone libraries in particular have aided in studies of microbial biodiversity and function, and allowed the mining of novel enzymes. Libraries are often constructed by cloning large inserts into cosmid or fosmid vectors. Recently, there have been reports of GC bias in fosmid metagenomic libraries, and it was speculated to be a result of fragmentation and loss of AT-rich sequences during cloning. However, evidence in the literature suggests that transcriptional activity or gene product toxicity may play a role.ResultsTo explore possible mechanisms responsible for sequence bias in clone libraries, we constructed a cosmid library from a human microbiome sample and sequenced DNA from different steps during library construction: crude extract DNA, size-selected DNA, and cosmid library DNA. We confirmed a GC bias in the final cosmid library, and we provide evidence that the bias is not due to fragmentation and loss of AT-rich sequences but is likely occurring after DNA is introduced into Escherichia coli. To investigate the influence of strong constitutive transcription, we searched the sequence data for promoters and found that rpoD/σ70 promoter sequences were underrepresented in the cosmid library. Furthermore, when we examined the genomes of taxa that were differentially abundant in the cosmid library relative to the original sample, we found the bias to be more correlated with the number of rpoD/σ70 consensus sequences in the genome than with simple GC content.ConclusionsThe GC bias of metagenomic libraries does not appear to be due to DNA fragmentation. Rather, analysis of promoter sequences provides support for the hypothesis that strong constitutive transcription from sequences recognized as rpoD/σ70 consensus-like in E. coli may lead to instability, causing loss of the plasmid or loss of the insert DNA that gives rise to the transcription. Despite widespread use of E. coli to propagate foreign DNA in metagenomic libraries, the effects of in vivo transcriptional activity on clone stability are not well understood. Further work is required to tease apart the effects of transcription from those of gene product toxicity.Electronic supplementary materialThe online version of this article (doi:10.1186/s40168-015-0086-5) contains supplementary material, which is available to authorized users.

Gaining insights into the codon usage patterns of TP53 gene across eight mammalian species.

TP53 gene is known as the “guardian of the genome” as it plays a vital role in regulating cell cycle, cell proliferation, DNA damage repair, initiation of programmed cell death and suppressing tumor growth. Non uniform usage of synonymous codons for a specific amino acid during translation of protein known as codon usage bias (CUB) is a unique property of the genome and shows species specific deviation. Analysis of codon usage bias with compositional dynamics of coding sequences has contributed to the better understanding of the molecular mechanism and the evolution of a particular gene. In this study, the complete nucleotide coding sequences of TP53 gene from eight different mammalian species were used for CUB analysis. Our results showed that the codon usage patterns in TP53 gene across different mammalian species has been influenced by GC bias particularly GC3 and a moderate bias exists in the codon usage of TP53 gene. Moreover, we observed that nature has highly favored the most over represented codon CTG for leucine amino acid but selected against the ATA codon for isoleucine in TP53 gene across all mammalian species during the course of evolution.

Non-crossover gene conversions show strong GC bias and unexpected clustering in humans.

Although the past decade has seen tremendous progress in our understanding of fine-scale recombination, little is known about non-crossover (NCO) gene conversion. We report the first genome-wide study of NCO events in humans. Using SNP array data from 98 meioses, we identified 103 sites affected by NCO, of which 50/52 were confirmed in sequence data. Overlap with double strand break (DSB) hotspots indicates that most of the events are likely of meiotic origin. We estimate that a site is involved in a NCO at a rate of 5.9 × 10(-6)/bp/generation, consistent with sperm-typing studies, and infer that tract lengths span at least an order of magnitude. Observed NCO events show strong allelic bias at heterozygous AT/GC SNPs, with 68% (58-78%) transmitting GC alleles (p = 5 × 10(-4)). Strikingly, in 4 of 15 regions with resequencing data, multiple disjoint NCO tracts cluster in close proximity (∼20-30 kb), a phenomenon not previously seen in mammals.

GROM-RD: resolving genomic biases to improve read depth detection of copy number variants.

Amplifications or deletions of genome segments, known as copy number variants (CNVs), have been associated with many diseases. Read depth analysis of next-generation sequencing (NGS) is an essential method of detecting CNVs. However, genome read coverage is frequently distorted by various biases of NGS platforms, which reduce predictive capabilities of existing approaches. Additionally, the use of read depth tools has been somewhat hindered by imprecise breakpoint identification. We developed GROM-RD, an algorithm that analyzes multiple biases in read coverage to detect CNVs in NGS data. We found non-uniform variance across distinct GC regions after using existing GC bias correction methods and developed a novel approach to normalize such variance. Although complex and repetitive genome segments complicate CNV detection, GROM-RD adjusts for repeat bias and uses a two-pipeline masking approach to detect CNVs in complex and repetitive segments while improving sensitivity in less complicated regions. To overcome a typical weakness of RD methods, GROM-RD employs a CNV search using size-varying overlapping windows to improve breakpoint resolution. We compared our method to two widely used programs based on read depth methods, CNVnator and RDXplorer, and observed improved CNV detection and breakpoint accuracy for GROM-RD. GROM-RD is available at http://grigoriev.rutgers.edu/software/.

Impact of three Illumina library construction methods on GC bias and HLA genotype calling

Next-generation sequencing (NGS) is increasingly recognized for its ability to overcome allele ambiguity and deliver high-resolution typing in the HLA system. Using this technology, non-uniform read distribution can impede the reliability of variant detection, which renders high-confidence genotype calling particularly difficult to achieve in the polymorphic HLA complex. Recently, library construction has been implicated as the dominant factor in instigating coverage bias. To study the impact of this phenomenon on HLA genotyping, we performed long-range PCR on 12 samples to amplify HLA-A, -B, -C, -DRB1, and -DQB1, and compared the relative contribution of three Illumina library construction methods (TruSeq Nano, Nextera, Nextera XT) in generating downstream bias. Here, we show high GC% to be a good predictor of low sequencing depth. Compared to standard TruSeq Nano, GC bias was more prominent in transposase-based protocols, particularly Nextera XT, likely through a combination of transposase insertion bias being coupled with a high number of PCR enrichment cycles. Importantly, our findings demonstrate non-uniform read depth can have a direct and negative impact on the robustness of HLA genotyping, which has clinical implications for users when choosing a library construction strategy that aims to balance cost and throughput with data quality.

Abstract 5175: Re-analysis of breast invasive carcinoma (BRCA) TCGA copy number data improves tumor profiles

Abstract Using the copy number and SNP- probes available in the TCGA level 1 data, reprocessed tumor profiles were more consistent with a control set in terms of median number of copy number events, sample ploidy, and breakpoint genes than the published “level 3” TCGA data. Probe-level data were analyzed using Nexus Copy Number SNP-FASST2 (a multi-state HMM algorithm that uses both SNP and copy number probes in making state assignments), with systematic correction applied to correct for GC biases. Additionally we performed manual baseline adjustment to correct for sample ploidy based on whole-genome B-alelle frequency data for each sample. The median number of copy number events in the TCGA data set was reduced from 377 (in the level 3 set) to 193, with a corresponding reduction in variance, and in line with our control samples (median CN changes of 173 and 142). After manual inspection, more than 15% of the TCGA BRCA samples available at level 3 were found to have incorrect baseline ploidy assignments. The resultant reanalyzed data set, along with associated clinical annotations for each sample, is available through Nexus DB repository to the general scientific community and should provide greater utility for further research. Citation Format: Louis J. Culot, Andrea O'Hara, Raja Keshavan, Zhiwei Che, Soheil Shams. Re-analysis of breast invasive carcinoma (BRCA) TCGA copy number data improves tumor profiles. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 5175. doi:10.1158/1538-7445.AM2014-5175

The deep phylogeny of jumping spiders (Araneae, Salticidae).

In order to resolve better the deep relationships among salticid spiders, we compiled and analyzed a molecular dataset of 169 salticid taxa (and 7 outgroups) and 8 gene regions. This dataset adds many new taxa to previous analyses, especially among the non-salticoid salticids, as well as two new genes – wingless and myosin heavy chain. Both of these genes, and especially the better sampled wingless, confirm many of the relationships indicated by other genes. The cocalodines are placed as sister to lapsiines, in a broader clade with the spartaeines. Cocalodines, lapsiines, and spartaeines are each supported as monophyletic, though the first two have no known morphological synapomorphies. The lyssomanines appear to be non-monophyletic, of three separate groups: (1) Lyssomanes plus Chinoscopus, (2) Onomastus, and (3) the remainder of Old World species. Several previously-inferred relationships continue to be supported: hisponines as sister to the Salticoida, Amycoida as sister to the remaining Salticoida, and Saltafresia as monophyletic. The relationship of Salticus with Philaeus and relatives is now considered well enough corroborated to move the latter into the subfamily Salticinae. A new clade consisting of the Plexippoida + Aelurilloida + Leptorchesteae + Salticinae is recognized. Nungia is found to be an astioid, and Echeclus, Gedea and Diplocanthopoda to be hasariines. The euophryines are corroborated as monophyletic. The agoriines Agorius and Synagelides are salticoids, within the sister group to amycoids, but their further placement is problematical, perhaps because of their nuclear ribosomal genes’ high GC bias, as also seen in the similarly problematic Eupoa.

OccuPeak: ChIP-Seq Peak Calling Based on Internal Background Modelling

ChIP-seq has become a major tool for the genome-wide identification of transcription factor binding or histone modification sites. Most peak-calling algorithms require input control datasets to model the occurrence of background reads to account for local sequencing and GC bias. However, the GC-content of reads in Input-seq datasets deviates significantly from that in ChIP-seq datasets. Moreover, we observed that a commonly used peak calling program performed equally well when the use of a simulated uniform background set was compared to an Input-seq dataset. This contradicts the assumption that input control datasets are necessary to fatefully reflect the background read distribution. Because the GC-content of the abundant single reads in ChIP-seq datasets is similar to those of randomly sampled regions we designed a peak-calling algorithm with a background model based on overlapping single reads. The application, OccuPeak, uses the abundant low frequency tags present in each ChIP-seq dataset to model the background, thereby avoiding the need for additional datasets. Analysis of the performance of OccuPeak showed robust model parameters. Its measure of peak significance, the excess ratio, is only dependent on the tag density of a peak and the global noise levels. Compared to the commonly used peak-calling applications MACS and CisGenome, OccuPeak had the highest sensitivity in an enhancer identification benchmark test, and performed similar in an overlap tests of transcription factor occupation with DNase I hypersensitive sites and H3K27ac sites. Moreover, peaks called by OccuPeak were significantly enriched with cardiac disease-associated SNPs. OccuPeak runs as a standalone application and does not require extensive tweaking of parameters, making its use straightforward and user friendly. Availability: http://occupeak.hfrc.nl

Performance Comparison between Rapid Sequencing Platforms for Ultra-Low Coverage Sequencing Strategy

Ultra-low coverage sequencing (ULCS) is one of the most promising strategies for sequencing based clinical application. These clinical applications, especially prenatal diagnosis, have a strict requirement of turn-around-time; therefore, the application of ULCS is restricted by current high throughput sequencing platforms. Recently, the emergence of rapid sequencing platforms, such as MiSeq and Ion Proton, brings ULCS strategy into a new era. The comparison of their performance could shed lights on their potential application in large-scale clinic trials. In this study, we performed ULCS (<0.1X coverage) on both MiSeq and Ion Proton platforms for 18 spontaneous abortion fetuses carrying aneuploidy and compared their performance on different levels. Overall basic data and GC bias showed no significant difference between these two platforms. We also found the sex and aneuploidy detection indicated 100% sensitivity and 100% specificity on both platforms. Our study generated essential data from these two rapid sequencing platforms, which provides useful reference for later research and potentially accelerates the clinical applications of ULCS.

Investigating and Correcting Plasma DNA Sequencing Coverage Bias to Enhance Aneuploidy Discovery

Pregnant women carry a mixture of cell-free DNA fragments from self and fetus (non-self) in their circulation. In recent years multiple independent studies have demonstrated the ability to detect fetal trisomies such as trisomy 21, the cause of Down syndrome, by Next-Generation Sequencing of maternal plasma. The current clinical tests based on this approach show very high sensitivity and specificity, although as yet they have not become the standard diagnostic test. Here we describe improvements to the analysis of the sequencing data by reducing GC bias and better handling of the genomic repeats. We show substantial improvements in the sensitivity of the standard trisomy 21 statistical tests, which we measure by artificially reducing read coverage. We also explore the bias stemming from the natural cleavage of plasma DNA by examining DNA motifs and position specific base distributions. We propose a model to correct this fragmentation bias and observe that incorporating this bias does not lead to any further improvements in the detection of fetal trisomy. The improved bias corrections that we demonstrate in this work can be readily adopted into existing fetal trisomy detection protocols and should also lead to improvements in sub-chromosomal copy number variation detection.

Performance comparison of four exome capture systems for deep sequencing.

BackgroundRecent developments in deep (next-generation) sequencing technologies are significantly impacting medical research. The global analysis of protein coding regions in genomes of interest by whole exome sequencing is a widely used application. Many technologies for exome capture are commercially available; here we compare the performance of four of them: NimbleGen’s SeqCap EZ v3.0, Agilent’s SureSelect v4.0, Illumina’s TruSeq Exome, and Illumina’s Nextera Exome, all applied to the same human tumor DNA sample.ResultsEach capture technology was evaluated for its coverage of different exome databases, target coverage efficiency, GC bias, sensitivity in single nucleotide variant detection, sensitivity in small indel detection, and technical reproducibility. In general, all technologies performed well; however, our data demonstrated small, but consistent differences between the four capture technologies. Illumina technologies cover more bases in coding and untranslated regions. Furthermore, whereas most of the technologies provide reduced coverage in regions with low or high GC content, the Nextera technology tends to bias towards target regions with high GC content.ConclusionsWe show key differences in performance between the four technologies. Our data should help researchers who are planning exome sequencing to select appropriate exome capture technology for their particular application.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-449) contains supplementary material, which is available to authorized users.

SeqCNA: an R package for DNA copy number analysis in cancer using high-throughput sequencing

BackgroundDeviations in the amount of genomic content that arise during tumorigenesis, called copy number alterations, are structural rearrangements that can critically affect gene expression patterns. Additionally, copy number alteration profiles allow insight into cancer discrimination, progression and complexity. On data obtained from high-throughput sequencing, improving quality through GC bias correction and keeping false positives to a minimum help build reliable copy number alteration profiles.ResultsWe introduce seqCNA, a parallelized R package for an integral copy number analysis of high-throughput sequencing cancer data. The package includes novel methodology on (i) filtering, reducing false positives, and (ii) GC content correction, improving copy number profile quality, especially under great read coverage and high correlation between GC content and copy number. Adequate analysis steps are automatically chosen based on availability of paired-end mapping, matched normal samples and genome annotation.ConclusionsseqCNA, available through Bioconductor, provides accurate copy number predictions in tumoural data, thanks to the extensive filtering and better GC bias correction, while providing an integrated and parallelized workflow.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-178) contains supplementary material, which is available to authorized users.

Efficiency of Corynebacterium pseudotuberculosis Cp31 Genome Assembly with the Hi-Q Enzyme on an Ion Torrent PGM Sequencing Platform

Despite the high accuracy obtained through high throughput sequencing (HTS) platforms, including sequencing methods such as pyrosequencing (Roche 454), ligase (SOLiD System), and recently post-light sequencing using the Ion Torrent PGM that is able to detect ions released during sequencing, there are still many errors inherent in the chemistry used by these platforms, for example, INDEL (Insertion/Deletion) that are very common on platforms 454 of Roche and Ion Torrent PGM; Substitution abound in Illumina platforms and SOLiD. Thus, efforts to address these problems have been undertaken by the sequencing companies. To improve the accuracy of the Ion Torrent PGM reads, Life Technologies has developed an enzyme called Hi-Q. This work aims to demonstrate the performance of the genome assembly of Corynebacterium pseudotuberculosis Cp31 using the enzyme Hi-Q. To evaluate the results, we used an Ion Torrent dataset obtained without the enzyme for the same strain. The sequencing using the Hi-Q enzyme affected the accuracy of the reads, improving the assembly quality. As a result, a high number of complete genes related to the reference genome were obtained compared to the previous data (without Hi-Q). Furthermore, the use of Hi-Q reduced the number of contigs. After evaluated the GC bias in the genome produced through the Hi-Q, we identified a reduction of GC-Bias, what can reduce the amount and size of gaps generated in the genome assembly process. Furthermore, the comparison of the amount of pseudogenes observed in the genome annotation of C. pseudotuberculosis 31 available at NCBI and the genome sequenced by Ion Torrent PGM with a Hi-Q enzyme, show 3-fold less pseudogenes in the genome obtained through Hi-Q enzyme. Thus, the high efficiency of Hi-Q was validated, which will be useful to whole-genome sequencing and RNASeq projects, due to its high accuracy, compared to the previous chemistry.

Summarizing Specific Profiles in Illumina Sequencing from Whole-Genome Amplified DNA

Advances in both high-throughput sequencing and whole-genome amplification (WGA) protocols have allowed genomes to be sequenced from femtograms of DNA, for example from individual cells or from precious clinical and archived samples. Using the highly curated Caenorhabditis elegans genome as a reference, we have sequenced and identified errors and biases associated with Illumina library construction, library insert size, different WGA methods and genome features such as GC bias and simple repeat content. Detailed analysis of the reads from amplified libraries revealed characteristics suggesting that majority of amplified fragment ends are identical but inverted versions of each other. Read coverage in amplified libraries is correlated with both tandem and inverted repeat content, while GC content only influences sequencing in long-insert libraries. Nevertheless, single nucleotide polymorphism (SNP) calls and assembly metrics from reads in amplified libraries show comparable results with unamplified libraries. To utilize the full potential of WGA to reveal the real biological interest, this article highlights the importance of recognizing additional sources of errors from amplified sequence reads and discusses the potential implications in downstream analyses.

New rRNA Gene-Based Phylogenies of the Alphaproteobacteria Provide Perspective on Major Groups, Mitochondrial Ancestry and Phylogenetic Instability

Bacteria in the class Alphaproteobacteria have a wide variety of lifestyles and physiologies. They include pathogens of humans and livestock, agriculturally valuable strains, and several highly abundant marine groups. The ancestor of mitochondria also originated in this clade. Despite significant effort to investigate the phylogeny of the Alphaproteobacteria with a variety of methods, there remains considerable disparity in the placement of several groups. Recent emphasis on phylogenies derived from multiple protein-coding genes remains contentious due to disagreement over appropriate gene selection and the potential influences of systematic error. We revisited previous investigations in this area using concatenated alignments of the small and large subunit (SSU and LSU) rRNA genes, as we show here that these loci have much lower GC bias than whole genomes. This approach has allowed us to update the canonical 16S rRNA gene tree of the Alphaproteobacteria with additional important taxa that were not previously included, and with added resolution provided by concatenating the SSU and LSU genes. We investigated the topological stability of the Alphaproteobacteria by varying alignment methods, rate models, taxon selection and RY-recoding to circumvent GC content bias. We also introduce RYMK-recoding and show that it avoids some of the information loss in RY-recoding. We demonstrate that the topology of the Alphaproteobacteria is sensitive to inclusion of several groups of taxa, but it is less affected by the choice of alignment and rate methods. The majority of topologies and comparative results from Approximately Unbiased tests provide support for positioning the Rickettsiales and the mitochondrial branch within a clade. This composite clade is a sister group to the abundant marine SAR11 clade (Pelagibacterales). Furthermore, we add support for taxonomic assignment of several recently sequenced taxa. Accordingly, we propose three subclasses within the Alphaproteobacteria: the Caulobacteridae, the Rickettsidae, and the Magnetococcidae.

An evaluation of the PacBio RS platform for sequencing and de novo assembly of a chloroplast genome

BackgroundSecond generation sequencing has permitted detailed sequence characterisation at the whole genome level of a growing number of non-model organisms, but the data produced have short read-lengths and biased genome coverage leading to fragmented genome assemblies. The PacBio RS long-read sequencing platform offers the promise of increased read length and unbiased genome coverage and thus the potential to produce genome sequence data of a finished quality containing fewer gaps and longer contigs. However, these advantages come at a much greater cost per nucleotide and with a perceived increase in error-rate. In this investigation, we evaluated the performance of the PacBio RS sequencing platform through the sequencing and de novo assembly of the Potentilla micrantha chloroplast genome.ResultsFollowing error-correction, a total of 28,638 PacBio RS reads were recovered with a mean read length of 1,902 bp totalling 54,492,250 nucleotides and representing an average depth of coverage of 320× the chloroplast genome. The dataset covered the entire 154,959 bp of the chloroplast genome in a single contig (100% coverage) compared to seven contigs (90.59% coverage) recovered from an Illumina data, and revealed no bias in coverage of GC rich regions. Post-assembly the data were largely concordant with the Illumina data generated and allowed 187 ambiguities in the Illumina data to be resolved. The additional read length also permitted small differences in the two inverted repeat regions to be assigned unambiguously.ConclusionsThis is the first report to our knowledge of a chloroplast genome assembled de novo using PacBio sequence data. The PacBio RS data generated here were assembled into a single large contig spanning the P. micrantha chloroplast genome, with a higher degree of accuracy than an Illumina dataset generated at a much greater depth of coverage, due to longer read lengths and lower GC bias in the data. The results we present suggest PacBio data will be of immense utility for the development of genome sequence assemblies containing fewer unresolved gaps and ambiguities and a significantly smaller number of contigs than could be produced using short-read sequence data alone.

Recombination Dynamics of a Human Y-Chromosomal Palindrome: Rapid GC-Biased Gene Conversion, Multi-kilobase Conversion Tracts, and Rare Inversions

The male-specific region of the human Y chromosome (MSY) includes eight large inverted repeats (palindromes) in which arm-to-arm similarity exceeds 99.9%, due to gene conversion activity. Here, we studied one of these palindromes, P6, in order to illuminate the dynamics of the gene conversion process. We genotyped ten paralogous sequence variants (PSVs) within the arms of P6 in 378 Y chromosomes whose evolutionary relationships within the SNP-defined Y phylogeny are known. This allowed the identification of 146 historical gene conversion events involving individual PSVs, occurring at a rate of 2.9–8.4×10−4 events per generation. A consideration of the nature of nucleotide change and the ancestral state of each PSV showed that the conversion process was significantly biased towards the fixation of G or C nucleotides (GC-biased), and also towards the ancestral state. Determination of haplotypes by long-PCR allowed likely co-conversion of PSVs to be identified, and suggested that conversion tract lengths are large, with a mean of 2068 bp, and a maximum in excess of 9 kb. Despite the frequent formation of recombination intermediates implied by the rapid observed gene conversion activity, resolution via crossover is rare: only three inversions within P6 were detected in the sample. An analysis of chimpanzee and gorilla P6 orthologs showed that the ancestral state bias has existed in all three species, and comparison of human and chimpanzee sequences with the gorilla outgroup confirmed that GC bias of the conversion process has apparently been active in both the human and chimpanzee lineages.