Bloginar: Standardizing Bioinformatics with BioHDF (HDF5)

Yesterday we (The HDF Group and Geospiza) released the BioHDF prototype software.  To mark the occasion, and demonstrate some of BioHDF’s capabilities and advantages, I share the poster we presented at this year’s AGBT (Advances in Genome Biology and Technology) conference.

The following map guides the presentation. The poster has a title and four main sections, which cover background information, specific aspects of the general Next Generation Sequencing (NGS) workflow, and HDF5’s advantages for working with large amounts of NGS data.

 

Section 1.  The first section introduces HDF5 (Hierarchical Data Format) as a software platform for working with scientific data.  The introduction begins with the abstract and lists five specific challenges created by NGS: 1) high end computing infrastructures are needed to work with NGS data, 2) NGS data analysis involves complex multi-step processes that, 3) compare NGS data to multiple reference sequence databases, 4) the resulting datasets of alignments must be visualized in multiple ways, and 5) scientific knowledge is gained when many datasets are compared. 

Next, choices for managing NGS data are compared in a four category table.  These include text and binary formats. While text formats (delimited and XML) have been popular for bioinformatics, they do not scale well and binary formats are gaining in popularity. The current bioinformatics binary formats are listed (bottom left) along with a description of their limitations. 

The introduction closes with a description of HDF5 and its advantages for supporting NGS data management and analysis. Specifically, HDF5 is platform for managing scientific data. Such data are typically complex and consist of images, large multi-dimensional arrays, and meta data. HDF5 has been used for over 20 years in other data intensive fields; it is robust, portable, and tuned for high performance computing. Thus HDF5 is well suited for NGS. Indeed, groups from academic researchers to NGS instrument vendors, and software companies are recognizing the value of HDF5.

Section 2. This section illustrates how HDF5 facilitates primary data analysis. First we are reminded that NGS data are analyzed in three phases: primary analysis, secondary analysis and tertiary analysis. Primary analysis is the step that converts images to reads consisting of basecalls (or colors, or flowgrams), and quality values. In secondary analysis, reads are aligned to reference data (mapped) or amongst themselves (assembled). In many NGS assays, secondary analysis produces tables of alignments that must be compared to one and other, in tertiary analysis, to gain scientific insights. 

The remaining portion of section 2 shows how Illumina GA and SOLiD primary data (reads and quality values) can be stored in BioHDF and later reviewed using the BioHDF tools and scripts.  The resulting quality graphs are organized into three groups (left to right) to show base composition plots, quality value (QV) distribution graphs, and other summaries.

Base composition plots show the count of each base (or color) that occurs at a given position in the read. These plots are used to assess overall randomness of a library and observe systematic nucleotide incorporation errors or biases.

Quality value plots show the distribution of QVs at each base position within the ensemble of reads. As each NGS run produces many millions of reads, it is worthwhile summarizing QVs in multiple ways. The first plots, from the top, show the average QV per base with error bars indicating QVs that are within one standard deviation of the mean. Next, box and whisker plots show the overall quality distribution (median, lower and upper quartile, minimum and maximum values) at each position. These plots are followed by “error” plots which show the total count of QVs below certain thresholds (red, QV < 10; green QV < 20; blue, QV < 30). The final two sets of plots show the number of QVs at each position for all observed values and the number of bases having each quality value.

The final group of plots show overall dataset complexity, GC content (base space only), average QV/read, and %GC vs average QV (base space only).  Dataset complexity is computed by determining the number of times a given read exactly matches other reads in the dataset. In some experiments, too many identical reads indicates a problem like PCR bias. In other cases, like tag profiling, many identical reads are expected from highly expressed genes. Errors in the data can artificially increase complexity.

Section 3.  Primary data analysis gives us a picture of how well the samples were prepared or how well the instrument ran with some indication about sample quality. Secondary and tertiary analysis tell us about sample quality and more importantly, provides biological insights. The third section focuses on secondary and tertiary analysis and begins with a brief cartoon showing a high level data analysis workflow using BioHDF to store primary data, alignment results, and annotations. BioHDF tools are used to query these data and other software within GeneSifter is used to compare data between samples and display the data in interactive reports to examine the details from single or multiple samples.

The left side of this section illustrates what is possible with single samples. Beginning with a simple table that indicates how many reads align to each reference sequence, we can drill into multiple reports that provide increasing detail about the alignments. For example, the gene list report (second from top) uses gene model annotations to summarize the alignments for all genes identified in the dataset. Each gene is displayed as a thumbnail graphic that can be clicked to see greater detail, which is shown in the third plot. The Integrated Gene View not only shows the density of reads across the gene's genomic region, but also shows evidence of splice junctions, and identified single base differences (SNVs) and small insertions and deletions (indels). Navigation controls provide ways to zoom into and out of the current view of data, and move to new locations. Additionally, when possible, the read density plot is accompanied by an Entrez gene model and dbSNP data so that data can be observed in a context of known information. Tables that describe the observed variants follow. Clicking on a variant drills into the alignment viewer to show the reads encompassing the point of variation.

The right side illustrates multi-sample analysis in GeneSifter. In assays like RNA-Seq, alignment tables are converted to gene expression values that can be compared between samples. Volcano (top) and other plots are used visualize the differences between the datasets. Since each point in the volcano plot represents the difference in expression for a gene between two samples (or conditions), we can click on that point to view the expression details for that gene (middle) in the different samples. In the case of RNA-Seq, we can also obtain expression values for the individual exons with the gene, making it possible to observe differential exon levels in conjunction with overall gene expression levels (middle). Clicking the appropriate link in the exon expression bar graph, takes us to the alignment details for the samples being analyzed (bottom), in this example we have two cases and two control replicates. Like the single sample Integrated Gene Views, annotations are displayed with alignment data. When navigation buttons are clicked all of the displayed genes move together so that you can explore the gene's details and surrounding neighborhood for multiple samples in a comparative fashion.

Section 4.  The poster closes with details about BioHDF.  First, the data model is described. An advantage of the BioHDF model is that read data are organized non-redundantly. Other formats, like BAM, tend to store reads with alignments and if a read has multiple alignments in a genome, or is aligned to multiple reference sequences, it gets stored multiple times. This may seem trivial, but anything that can happen a million times, becomes noticeable. This fact is demonstrated in the in table listed in the second panel “High Performance Computing Advantages.”  Other HDF5 advantages are listed below the performance stats table.  Most notably is HDF5’s ability to easily support multiple indexing schemes like nested containment lists (NClists). NClists solve the problem of efficiently accessing reads from alignments that may be contained in other alignments, which I will save for a later post.

Finally, the poster is summarized with a number of take home points. These reiterate the fact that NGS is driving the need to use binary file formats to manage NGS and analysis results and that HDF5 provides an attractive solution because of its long history and development efforts that specifically target scientific programming requirements. In our hands, HDF5 has helped make GeneSifter a highly scalable and interactive web-application with less development effort than would have been needed to implement other technologies.  

If you are software developer and are interested in BioHDF please visit www.biohdf.org.  If you do not want to program and instead, want a way to easily analyze your NGS data to make new discoveries, please contact us. 

Expeditiously Exponential: Data Sharing and Standardization

We can all agree that our ability to produce genomics and other kinds of data is increasing at exponential rates. Less clear, is understanding the consequences for how these data will be shared and ultimately used. These topics were explored in last month's (Oct. 9, 2009) policy forum feature in the journal Science.

The first article, listed under the category "megascience," dealt with issues about sharing 'omics data. The challenge being that systems biology research demands that data from many kinds of instrument platforms (DNA sequencing, mass spectrometry, flow cytometry, microscopy, and others) be combined in different ways to produce a complete picture of a biological system. Today, each platform generates its own kind of "big" data that, to be useful, must be computationally processed and transformed into standard outputs. Moreover, the data are often collected by different research groups focused on particular aspects of a common problem. Hence, the full utility of the data being produced can only be realized when the data are made open and shared throughout the scientific community. The article listed past efforts in developing sharing policies and the central table included 12 data sharing policies that are already in effect.

Sharing data solves half of the problem, the other aspect is being able to use the data once shared. This requires that data be structured and annotated in ways that make it understandable by a wide range of research groups. Such standards typically include minimum information check lists that define specific annotations, and which data should be kept from different platforms. The data and metadata are stored in structured documents that reflect a community's view about what is important to know with respect to how data were collected and the samples the data were collected from. The problem is that annotation standards are developed by diverse groups and, like the data, are expanding. This expansion creates new challenges with making data interoperable; the very problem standards try to address.

The article closed with high-level recommendations for enforcing policy through funding and publication requirements and acknowledged that full compliance requires that general concerns with pre-publication data use and patient information be addressed. More importantly, the article acknowledged that meeting data sharing and formatting standards has economic implications. That is, researches need time-efficient data management systems, the right kinds of tools and informatics expertise to meet standards. We also need to develop the right kind of global infrastructure to support data sharing.

Fortunately complying with data standards is an area where Geospiza can help. First, our software systems rely on open, scientifically valid tools and technologies. In DNA sequencing we support community developed alignment algorithms. The statistical analysis tools in GeneSifter Analysis Edition utilize R and BioConductor to compare gene expression data from both microarrays and DNA sequencing. Further, we participate in the community by contributing additional open-source tools and standards through efforts like the BioHDF project. Second, the GeneSifter Analysis and Laboratory platforms provide the time-effiecient data management solutions needed to move data through its complete life cycle from collection, to intermediate analysis, to publishing files in standard formats.

GeneSifter lowers researcher's economic barriers of meeting data sharing and annotation standards keep the focus on doing good science with the data.

Open or Closed

A key aspect of Geospiza’s software development and design strategy is to incorporate open scientific technologies into the GeneSifter products to deliver user friendly access to best-of-breed tools used to manage and analyze genetic data from DNA sequencing, microarray, and other experiments.

Open scientific technologies include open-source and published academic algorithms, programs, databases, and core infrastructure software such as operating systems, web servers, and other components needed to build modern systems for data management. Unlike approaches that rely on proprietary software, Geospiza’s adoption of open platforms and participation in the open-source community benefits our customers in numerous ways.

Geospiza’s Open Source History

When Geospiza began in 1997, the company started building software systems to support DNA sequencing technologies and applications. Our first products focused on web-enabled data management for DNA sequencing-based genomics applications. Foundational infrastructure, such as the web-server, and application layer incorporated Apache and Perl. We were also leaders, in that our first systems operated on Linux, an open-source UNIX-based operating system. In those early days, however, we used proprietary databases such as Solid and Oracle because the open-source alternatives Postgres and MySQL were still lacking features needed to support robust data processing environments. As these products matured, we extended our application support to include Postgres to deliver cost-effective solutions for our customers. By adopting such open platforms we were able to deliver robust, high performing systems, rapidly at a reasonable cost.

In addition to using open-source technology as the foundation of our infrastructure, we also worked with open tools to deliver our scientific applications. Our first product, the Finch Blast-Server, utilized the public domain BLAST from NCBI. Where possible, we sought to include well-adopted tools for other applications such as base calling and sequence assembly and repeat masking, for which the source code was made available. We favored these kinds tools over developing our own proprietary tools, because it was clear that technologies emerging from communities like the genome centers would advance much quicker and be better tuned to the problems people were trying to address. Further, these tools, because of their wide adoption within their community and publication, received higher levels of scrutiny and validation than their proprietary counterparts.

Times Change

In the early days, many of the genome center tools were licensed by universities. As the bioinformatics field matured, open-source models for delivering bioinformatics software have become more popular. Led by NCBI and pioneered by organizations like TIGR (now JCVI) and the Sanger institute, the majority of useful bioinformatics programs are now being delivered open-source either under GPL, BSD like, or Perl Artistic style licenses (www.opensource.org). The authors of these programs have benefited from wider adoption of their programs and continued support from funding agencies like NIH. In some cases other groups are extending best-of-breed technologies into new applications.

A significant benefit of the increasing use of open-source licensing is that a large number of analytical tools are readily available for many kinds of applications. Today we have robust statistical platforms like R and BioConductor and several algorithms for aligning Next Gen Sequencing (NGS) data. Because these platforms and tools are truly open-source, bioinformatics groups can easily access these technologies to understand how they work and compare other approaches to their own. This creates a competitive environment for bioinformatics tool providers that drives improvements in algorithm performance and accuracy and the research community benefits greatly.

Design Choices

Early on, Geospiza recognized value incorporating tools from the academic research community into our user friendly software systems. Such tools were being developed in close collaboration with the data production centers that were trying to solve scientific problems associated with DNA sequence assembly and analysis. Companies developing proprietary tools designed to compete with these efforts were at a disadvantage, because they did not have real time access to conversations between biologists, lab specialists, and mathematicians needed to quickly develop the deep experience of working with biologically complex data. This disadvantage continues today. Further, the closed nature of proprietary software limits the ability to publish work and have critical peer review of the code needed to ensure scientific validation.

Our work could proceed more quickly because we did not have to invest in solving the research problems associated with developing algorithms. Moreover, we did not have to invest in proving the scientific credibility of an algorithm. Instead we could cite published references and keep our focus on solving problems associated delivering the user interfaces needed to work with the data. Our customers benefited by gaining easy access to best-of-breed tools and having the knowledge that they had a community to draw on to understand their scientific basis.

Geospiza continues its practice of adopting open best-of-breed technologies. Our NGS systems utilize multiple tools such as MAQ, BWA, Bowtie, MapReads and others. GeneSifter Analysis Edition utilizes routines from the R and BioConductor package to perform statistical computations to compare datasets from microarray and NGS experiments. In addition, we are addressing issues related to high performance computing through our collaboration with the HDF Group and the BioHDF project. In this case we are not only adopting open-source technology, but also working with leaders in the field to make open-source contributions of our own.

When you use Geospiza’s GeneSifter products you can be assured that you are using the same tools as the leaders in our fields to receive the benefits of reducing data analysis costs combined with the advantages of community support through forums and peer reviewed literature.