Linkage analysis in the next-generation sequencing era

Linkage analysis was developed to detect excess co-segregation of the putative alleles underlying a phenotype with the alleles at a marker locus in family data. Many different variations of this analysis and corresponding study design have been developed to detect this co-segregation. Linkage studies have been shown to have high power to detect loci that have alleles (or variants) with a large effect size, i.e. alleles that make large contributions to the risk of a disease or to the variation of a quantitative trait. However, alleles with a large effect size tend to be rare in the population. In contrast, association studies are designed to have high power to detect common alleles which tend to have a small effect size for most diseases or traits. Although genome-wide association studies have been successful in detecting many new loci with common alleles of small effect for many complex traits, these common variants often do not explain a large proportion of disease risk or variation of the trait. In the past, linkage studies were successful in detecting regions of the genome that were likely to harbor rare variants with large effect for many simple Mendelian diseases and for many complex traits. However, identifying the actual sequence variant(s) responsible for these linkage signals was challenging because of difficulties in sequencing the large regions implicated by each linkage peak. Current 'next-generation' DNA sequencing techniques have made it economically feasible to sequence all exons or the whole genomes of a reasonably large number of individuals. Studies have shown that rare variants are quite common in the general population, and it is now possible to combine these new DNA sequencing methods with linkage studies to identify rare causal variants with a large effect size. A brief review of linkage methods is presented here with examples of their relevance and usefulness for the interpretation of whole-exome and whole-genome sequence data.     

The current status and portability of our sequence handling software.

I describe the current status of our sequence analysis software. The package contains a comprehensive suite of programs for managing large shotgun sequencing projects, a program containing 61 functions for analysing single sequences and a program for comparing pairs of sequences for similarity. The programs that have been described before have been improved by the addition of new functions and by being made very much easier to use. The major interactive programs have 125 pages of online help available from within them. Several new programs are described including screen editing of aligned gel readings for shotgun sequencing projects; a method to highlight errors in aligned gel readings, new methods for searching for putative signals in sequences. We use the programs on a VAX computer but the whole package has been rewritten to make it easy to transport it to other machines. I believe the programs will now run on any machine with a FORTRAN77 compiler and sufficient memory. We are currently putting the programs onto an IBM PC XT/AT and another micro running under UNIX.     

高通量测序技术结合正向遗传学手段在基因定位研究中的应用

传统的利用正向遗传学方法的基因定位一般是通过构建遗传连锁图谱进行的,该过程步骤繁琐、耗时耗力,很多情形下定位精确度低、区间大。随着高通量测序技术的快速发展以及测序成本的不断降低,多种简单快捷的利用测序手段定位基因的方法被开发出来,包括对突变体基因组直接测序定位、突变体材料构建混池测序定位和遗传分离群体测序构建图谱定位等,还可以对转录组和部分基因组进行测序定位。这些方法可以在核苷酸水平鉴定突变位点,并已推广到复杂的遗传背景中。近期报道的一些测序定位甚至是在不依赖于参考基因组序列、遗传杂交和连锁信息的情况下完成的,这使得很多非模式物种也能开展正向遗传学研究。本文就这些新技术及其在基因定位中的应用进行了综述。     

DNA sequence quality trimming and vector removal.

MOTIVATION: Most sequence comparison methods assume that the data being compared are trustworthy, but this is not the case with raw DNA sequences obtained from automatic sequencing machines. Nevertheless, sequence comparisons need to be done on them in order to remove vector splice sites and contaminants. This step is necessary before other genomic data processing stages can be carried out, such as fragment assembly or EST clustering. A specialized tool is therefore needed to solve this apparent dilemma. RESULTS: We have designed and implemented a program that specifically addresses the problem. This program, called LUCY, has been in use since 1998 at The Institute for Genomic Research (TIGR). During this period, many rounds of experience-driven modifications were made to LUCY to improve its accuracy and its ability to deal with extremely difficult input cases. We believe we have finally obtained a useful program which strikes a delicate balance among the many issues involved in the raw sequence cleaning problem, and we wish to share it with the research community. AVAILABILITY: LUCY is available directly from TIGR (http://www.tigr.org/softlab). Academic users can download LUCY after accepting a free academic use license. Business users may need to pay a license fee to use LUCY for commercial purposes. CONTACT: Questions regarding the quality assessment module of LUCY should be directed to Michael Holmes (mholmes@tigr.org). Questions regarding other aspects of LUCY should be directed to Hui-Hsien Chou (hhchou@iastate.edu).     

Primer design for large scale sequencing.

We have developed PRIDE, a primer design program that automatically designs primers in single contigs or whole sequencing projects to extend the already known sequence and to double strand single-stranded regions. The program is fully integrated into the Staden package (GAP4) and accessible with a graphical user interface. PRIDE uses a fuzzy logic-based system to calculate primer qualities. The computational performance of PRIDE is enhanced by using suffix trees to store the huge amount of data being produced. A test set of 110 sequencing primers and 11 PCR primer pairs has been designed on genomic templates, cDNAs and sequences containing repetitive elements to analyze PRIDE's success rate. The high performance of PRIDE, combined with its minimal requirement of user interaction and its fast algorithm, make this program useful for the large scale design of primers, especially in large sequencing projects.     

Removing Noise From Pyrosequenced Amplicons

Background  

In many environmental genomics applications a homologous region of DNA from a diverse sample is first amplified by PCR and then sequenced. The next generation sequencing technology, 454 pyrosequencing, has allowed much larger read numbers from PCR amplicons than ever before. This has revolutionised the study of microbial diversity as it is now possible to sequence a substantial fraction of the 16S rRNA genes in a community. However, there is a growing realisation that because of the large read numbers and the lack of consensus sequences it is vital to distinguish noise from true sequence diversity in this data. Otherwise this leads to inflated estimates of the number of types or operational taxonomic units (OTUs) present. Three sources of error are important: sequencing error, PCR single base substitutions and PCR chimeras. We present AmpliconNoise, a development of the PyroNoise algorithm that is capable of separately removing 454 sequencing errors and PCR single base errors. We also introduce a novel chimera removal program, Perseus, that exploits the sequence abundances associated with pyrosequencing data. We use data sets where samples of known diversity have been amplified and sequenced to quantify the effect of each of the sources of error on OTU inflation and to validate these algorithms.     

Fast identification and removal of sequence contamination from genomic and metagenomic datasets

High-throughput sequencing technologies have strongly impacted microbiology, providing a rapid and cost-effective way of generating draft genomes and exploring microbial diversity. However, sequences obtained from impure nucleic acid preparations may contain DNA from sources other than the sample. Those sequence contaminations are a serious concern to the quality of the data used for downstream analysis, causing misassembly of sequence contigs and erroneous conclusions. Therefore, the removal of sequence contaminants is a necessary and required step for all sequencing projects. We developed DeconSeq, a robust framework for the rapid, automated identification and removal of sequence contamination in longer-read datasets (150 bp mean read length). DeconSeq is publicly available as standalone and web-based versions. The results can be exported for subsequent analysis, and the databases used for the web-based version are automatically updated on a regular basis. DeconSeq categorizes possible contamination sequences, eliminates redundant hits with higher similarity to non-contaminant genomes, and provides graphical visualizations of the alignment results and classifications. Using DeconSeq, we conducted an analysis of possible human DNA contamination in 202 previously published microbial and viral metagenomes and found possible contamination in 145 (72%) metagenomes with as high as 64% contaminating sequences. This new framework allows scientists to automatically detect and efficiently remove unwanted sequence contamination from their datasets while eliminating critical limitations of current methods. DeconSeq's web interface is simple and user-friendly. The standalone version allows offline analysis and integration into existing data processing pipelines. DeconSeq's results reveal whether the sequencing experiment has succeeded, whether the correct sample was sequenced, and whether the sample contains any sequence contamination from DNA preparation or host. In addition, the analysis of 202 metagenomes demonstrated significant contamination of the non-human associated metagenomes, suggesting that this method is appropriate for screening all metagenomes. DeconSeq is available at http://deconseq.sourceforge.net/.     

Annotating individual human genomes

Advances in DNA sequencing technologies have made it possible to rapidly, accurately and affordably sequence entire individual human genomes. As impressive as this ability seems, however, it will not likely amount to much if one cannot extract meaningful information from individual sequence data. Annotating variations within individual genomes and providing information about their biological or phenotypic impact will thus be crucially important in moving individual sequencing projects forward, especially in the context of the clinical use of sequence information. In this paper we consider the various ways in which one might annotate individual sequence variations and point out limitations in the available methods for doing so. It is arguable that, in the foreseeable future, DNA sequencing of individual genomes will become routine for clinical, research, forensic, and personal purposes. We therefore also consider directions and areas for further research in annotating genomic variants.     

A QTL resource and comparison tool for pigs: PigQTLDB

During the past decade, efforts to map quantitative trait loci (QTL) in pigs have resulted in hundreds of QTL being reported for growth, meat quality, reproduction, disease resistance, and other traits. It is a challenge to locate, interpret, and compare QTL results from different studies. We have developed a pig QTL database (PigQTLdb) that integrates available pig QTL data in the public domain, thus, facilitating the use of this QTL data in future studies. We also developed a pig trait classification system to standardize names of traits and to simplify organization and searching of the trait data. These steps made it possible to compare primary data from diverse sources and methods. We used existing pig map databases and other publicly available data resources (such as PubMed) to avoid redundant developmental work. The PigQTLdb was also designed to include data representing major genes and markers associated with a large effect on economically important traits. To date, over 790 QTL from 73 publications have been curated into the database. Those QTL cover more than 300 different traits. The data have been submitted to the Entrez Gene and the Map Viewer resources at NCBI, where the information about markers was matched to marker records in NCBI’s UniSTS database. Having these data in a public resource like NCBI allows regularly updated automatic matching of markers to public sequence data by e-PCR. The submitted data, and the results of these calculations, are retrievable from NCBI via Entrez Gene, Map Viewer, and UniSTS. Efforts were undertaken to improve the integrated functional genomics resources for pigs.     

Inferring protein function by domain context similarities in protein-protein interaction networks

Background  

Genome sequencing projects generate massive amounts of sequence data but there are still many proteins whose functions remain unknown. The availability of large scale protein-protein interaction data sets makes it possible to develop new function prediction methods based on protein-protein interaction (PPI) networks. Although several existing methods combine multiple information resources, there is no study that integrates protein domain information and PPI networks to predict protein functions.     

Stepping stones in DNA sequencing

In recent years there have been tremendous advances in our ability to rapidly and cost-effectively sequence DNA. This has revolutionized the fields of genetics and biology, leading to a deeper understanding of the molecular events in life processes. The rapid technological advances have enormously expanded sequencing opportunities and applications, but also imposed strains and challenges on steps prior to sequencing and in the downstream process of handling and analysis of these massive amounts of sequence data. Traditionally, sequencing has been limited to small DNA fragments of approximately one thousand bases (derived from the organism's genome) due to issues in maintaining a high sequence quality and accuracy for longer read lengths. Although many technological breakthroughs have been made, currently the commercially available massively parallel sequencing methods have not been able to resolve this issue. However, recent announcements in nanopore sequencing hold the promise of removing this read-length limitation, enabling sequencing of larger intact DNA fragments. The ability to sequence longer intact DNA with high accuracy is a major stepping stone towards greatly simplifying the downstream analysis and increasing the power of sequencing compared to today. This review covers some of the technical advances in sequencing that have opened up new frontiers in genomics.     

Microbial Interaction Network Estimation via Bias-Corrected Graphical Lasso

With the increasing availability of microbiome 16S data, network estimation has become a useful approach to studying the interactions between microbial taxa. Network estimation on a set of variables is frequently explored using graphical models, in which the relationship between two variables is modeled via their conditional dependency given the other variables. Various methods for sparse inverse covariance estimation have been proposed to estimate graphical models in the high-dimensional setting, including graphical lasso. However, current methods do not address the compositional count nature of microbiome data, where abundances of microbial taxa are not directly measured, but are reflected by the observed counts in an error-prone manner. Adding to the challenge is that the sum of the counts within each sample, termed “sequencing depth,” is an experimental technicality that carries no biological information but can vary drastically across samples. To address these issues, we develop a new approach to network estimation, called BC-GLASSO (bias-corrected graphical lasso), which models the microbiome data using a logistic normal multinomial distribution with the sequencing depths explicitly incorporated, corrects the bias of the naive empirical covariance estimator arising from the heterogeneity in sequencing depths, and builds the inverse covariance estimator via graphical lasso. We demonstrate the advantage of BC-GLASSO over current approaches to microbial interaction network estimation under a variety of simulation scenarios. We also illustrate the efficacy of our method in an application to a human microbiome data set.

     

Molecular Markers and Concepts of Plant Evolutionary Relationships: Progress,Promise, and Future Prospects

In just the past 20 years systematics has progressed from the sequencing of individual genes for a few taxa to routine sequencing of complete plastid and even nuclear genomes. Recent technological advances have made it possible to compile very large data sets, the analyses of which have in turn provided unprecedented insights into phylogeny and evolution. Indeed, this narrow window of a few decades will likely be viewed as a golden era in systematics. Relationships have been resolved at all taxonomic levels across all groups of photosynthetic life. In the angiosperms, problematic deep-level relationships have either been largely resolved, or will be resolved within the next several years. The same large data sets have also provided new insights into the many rapid radiations that have characterized angiosperm evolution. For example, all of the major lineages of angiosperms likely arose within a narrow window of just a few million years. At the population level, the ease of DNA sequencing has given new life to phylogeographic studies, and microsatellite analyses have become more commonplace, with a concomitant impact on conservation and population biology. With the wealth of sequence data soon to be available, we are on the cusp of assembling the first semi-comprehensive tree of life for many of the 15,000 genera of flowering plants and indeed for much of green life. Accompanying these opportunities are also enormous new computational/informatic challenges including the management and phylogenetic analysis of such large, sometimes fragmentary data sets, and visualization of trees with thousands of terminals.     

SUGAR: graphical user interface-based data refiner for high-throughput DNA sequencing

Background

Next-generation sequencers (NGSs) have become one of the main tools for current biology. To obtain useful insights from the NGS data, it is essential to control low-quality portions of the data affected by technical errors such as air bubbles in sequencing fluidics.

Results

We develop a software SUGAR (subtile-based GUI-assisted refiner) which can handle ultra-high-throughput data with user-friendly graphical user interface (GUI) and interactive analysis capability. The SUGAR generates high-resolution quality heatmaps of the flowcell, enabling users to find possible signals of technical errors during the sequencing. The sequencing data generated from the error-affected regions of a flowcell can be selectively removed by automated analysis or GUI-assisted operations implemented in the SUGAR. The automated data-cleaning function based on sequence read quality (Phred) scores was applied to a public whole human genome sequencing data and we proved the overall mapping quality was improved.

Conclusion

The detailed data evaluation and cleaning enabled by SUGAR would reduce technical problems in sequence read mapping, improving subsequent variant analysis that require high-quality sequence data and mapping results. Therefore, the software will be especially useful to control the quality of variant calls to the low population cells, e.g., cancers, in a sample with technical errors of sequencing procedures.