Next Generation Sequencing and Bioinformatics

차세대 염기서열 분석기법과 생물정보학

Abstract

With the ongoing development of next-generation sequencing (NGS) platforms and advancements in the latest bioinformatics tools at an unprecedented pace, the ultimate goal of sequencing the human genome for less than $1,000 can be feasible in the near future. The rapid technological advances in NGS have brought about increasing demands for statistical methods and bioinformatics tools for the analysis and management of NGS data. Even in the early stages of the commercial availability of NGS platforms, a large number of applications or tools already existed for analyzing, interpreting, and visualizing NGS data. However, the availability of this plethora of NGS data presents a significant challenge for storage, analyses, and data management. Intrinsically, the analysis of NGS data includes the alignment of sequence reads to a reference, base-calling, and/or polymorphism detection, de novo assembly from paired or unpaired reads, structural variant detection, and genome browsing. While the NGS technologies have allowed a massive increase in available raw sequence data, a number of new informatics challenges and difficulties must be addressed to improve the current state and fulfill the promise of genome research. This review aims to provide an overview of major NGS technologies and bioinformatics tools for NGS data analyses.

매우 빠른 속도로 발전하고 있는 차세대 염기서열 분석 플랫폼과 최신 생물정보학적 분석도구들로 말미암아, 1,000달러 이하의 가격으로 인간 유전체 염기서열을 해독하고자 하는 궁극적인 목표가 조만간 곧 실현될 수 있을 것 같다. 차세대 염기서열 분석 분야의 급속한 기술적 진전은 NGS 데이터의 분석과 관리를 위한 통계적 방법과 생물정보학적 분석도구들에 대한 수요를 꾸준히 증대시키고 있다. NGS 플랫폼이 상용화되어 쓰이기 시작한 초창기부터, NGS 데이터를 분석하고 해석하거나, 가시화 해주는 다수의 응용프로그램이나 도구들이 개발되어 활용되어 왔다. 그러나, NGS 데이터의 엄청난 범람으로 데이터 저장, 데이터 분석 및 관리 등에 있어서 해결해야 할 많은 문제들이 부각되고 있다. NGS 데이터 분석은 단편서열과 참조서열간의 서열정렬, 염기식별, 다형성 발견, 쌍단편 서열이나 비쌍단편 서열 등을 이용한 어셈블리 작업, 구조변이 발견, 유전체 브라우징 등을 본질적으로 포함한다. 본 논문은 주요 차세대 염기서열 결정기술과 NGS 데이터 분석을 위한 생물정보학적 분석도구들에 대해 개관적으로 소개하고자 한다.

Introduction

The advent of next generation sequencing (NGS) has allowed an explosion in sequencing whole genomes of a wide range of organisms, with immense biological implications. NGS technologies, coupled with additional technological advances, have allowed sequencing genomes at much lower costs and much higher throughput than conventional sequencing method and have transformed the landscape of genomics [46]. In this context, they have lately provided insight into whole-genome characterization of a wide range of organisms. In the post-genomic era, new technologies have revealed an outbreak of prerequisite genomic sequences and supporting data to understand genome wide functional regulation of gene expression and metabolic pathways reconstruction [19]. For the future direction, we need the in-depth genome sequence information and analysis for most of the mammals, including human to fully understand genome variation of economic traits, genetic susceptibility to diseases, and pharmacogenomics of drug response. Recently, tremendous success has been achieved in the fields of decoding human genome, technological advancement of new era of human genome applications, toward personalized genomes and discovery of rare variants, leveraging genome sequencing to impact on cancer researches and mammalian evolution and population structure. The horizons and expectations have broadened due to the technological advances in the field of genomics, especially the high throughput NGS and its wide range of applications [43].

Most NGS platforms are based on various implementations of cyclic-array sequencing that sequences a dense array of DNA features by iterative cycles of enzymatic manipulation and imaging-based data collection [38]. The representative NGS platforms that are based on this sequencing technology include Roche's 454, Illumina's Genome Analyzer, ABI (Applied Biosystems)'s SOLiD and the Heliscope from Helicos. Although these platforms are quite diverse in sequencing biochemistry as well as in how the array is generated, their work flows are conceptually very similar. All of them allow the sequencing of millions of short sequences or reads simultaneously, and are capable of sequencing a full human genome per day at a cost of 200-fold less than previous methods. Moreover, they allow the generation of many kinds of sequence data and have its wide range of applications such as: chromatin immunoprecipitation coupled to DNA microarray (ChIPchip) or sequencing (ChIPseq) [44], RNA sequencing (RNA-seq) [39, 50], whole genome genotyping, de novo assembling and re-assembling of genome [41, 50], genome wide structural variation, mutation detection and carrier screening, detection of inherited disorders and complex human diseases, DNA library preparation, paired ends and genomic captures, sequencing of mitochondrial genome and personal genomics [43].

With the rapid advancement in NGS technologies and the subsequently fast growing volume of biological data, diverse data sources (databases and web servers) have been developed to facilitate data management, accessibility, and analysis. As acquisition of genomic data becomes increasingly cost-efficient, genomic data sets are accumulating at an exponential rate and new types of genetic data are emerging. These come with the inherent challenges of new methods of statistical analysis and modeling. Indeed new technologies are producing data at a rate that outpaces our ability to analyze its biological meaning. Researchers are addressing this challenge by adopting mathematical and statistical software, computer modeling, and other computational and engineering methods. As a result, bioinformatics has become the latest engineering discipline. As computers provide the ability to process the complex models, high-performance computer languages have become a necessity for implementing state-of-the-art algorithms and methods [19]. In the following sections of this review, the NGS technologies will be described and then intrinsic bioinformatics tools for NGS data analysis will be focused on subsequently.

 

Next Generation Sequencing Technologies

In 2000, Jonathan Rothberg founded 454 Life Sciences, which further developed the first commercially available NGS platform, the GS 20. The GS instrument was introduced in 2005, developed by 454 Life Sciences, as the first NGS system on the market. In the following years, Roche Applied Science acquired 454 Life Sciences and extended further the new version of the 454 instrument, i.e., the GS FLX titanium. Sharing the same technological principle in both GS 20 and GS FLX titanium, the flow cell is referred to as a "picotiter well" plate, which is made from a fused fiber-optic bundle. On a separate front, single-molecule PCR in micro-compartments consisting of water-in-oil emulsions was also developed by Roche high throughput NGS platform [48].

DNA sequencing with commercially available NGS platforms is generally conducted with the following steps. The first step of the sequencing process consists of genomic DNA fragmentation and ligation to common adaptors. In this first step, all of the NGS technologies are able to use alternative protocols in order to generate jumping libraries of matepaired tags with controllable distance distributions. After fragmentation and ligation with common adaptors, genomic DNA is then subjected to one of the several protocols that results in an array of millions of spatially immobilized PCR colonies. While these steps are followed in most NGS platforms, each utilizes a different strategy. This step can be achieved by several approaches, including in situ polonies, emulsion PCR or bridge PCR. Once the PCR colonies are immobilized in the array, the sequencing process itself consists of alternating cycles of enzyme-driven biochemistry and imaging-based data acquisition. NGS parallelization of the sequencing reactions generates hundreds of megabases to gigabases of nucleotide sequence reads in a single instrument run. This has enabled a drastic increase in available sequence data and fundamentally changed genome sequencing approaches in the biomedical sciences. Newly emerging NGS technologies and instruments have further contributed to a significant decrease in the cost of sequencing nearing the mark of $1,000 per genome sequencing.

The amount of introduced errors is correlated with the fidelity of the polymerase utilized in the reaction [14, 41]. Read lengths vary with the technology, pyrosequencing generating long reads (~400 nts), while reverse termination and sequencing by ligation technologies produce shorter reads. Different technologies can thus result in significantly different output data and performance. The combination of more than one platform is potentially more cost effective and could yield higher fidelity and accuracy [7, 15].

Massively parallel sequencing platforms commercially available and their features are summarized in the table 1. As the pace of NGS technologies is advancing rapidly, technical specifications and pricing are in flux and the race for more additional platforms are continuously on the horizon.

Table 1.Run times and gigabase (Gb) output per run for single-end sequencing are noted Run times and outputs approximately double when performing paired-end sequencing (http://en.wikipedia.org/wiki/Massive_parallel_sequencing).

Second Generation NGS Platforms

The second generation NGS platforms can generate about five hundred million bases of raw sequence (Roche) to billions of bases in a single run (Illumina, SOLiD). These novel methods rely on parallel, cyclic interrogation of sequences from spatially separated clonal amplicons (26 μm oil-aqueous emulsion bead [Roche: pyrosequencing chemistry], 1 μm clonal bead [SOLiD: sequencing by sequential ligation of oligonucleotide probes], clonal bridge [Illumina: sequencing by reversible dye terminators]). Currently, these three leading second generation NGS platforms (Fig. 1) are commercially available and the race for more additional platforms are continuously on the horizon [32].

Fig. 1.Advanced technological features of three leading second generation NGS platforms [43].

In Roche sequencing system [18], DNA fragments are ligated to beads by means of specific adapters. To obtain sufficient light signal intensity for detection in the sequencing- by-synthesis reaction step, emulsion PCR is carried out for amplification. Once the PCR amplification cycles are complete, each bead with its fragment is placed at the top end of an optical fiber that has the other end facing to a sensitive CCD camera, which enables the positional detection of emitted light. In the last step, polymerase enzyme and primer are added to the beads so that the synthesis of the complementary strand can start: the incorporation of a base by the polymerase enzyme in the growing chain releases a pyrophosphate group, which can be detected as emitted light. A limitation of the Roche 454 platform is that base calling cannot properly interpret long stretches (＞6) of the same nucleotide (homopolymer DNA segments).

The Illumina platform is the most widely available high throughput sequencing (HTS) technology. In this platform, the amplified sequencing features are generated by bridge PCR [1, 10] and after immobilization in the array, all the molecules are sequenced in parallel by means of sequencing by synthesis. During the sequencing process, each nucleotide is recorded through imaging techniques, and is then converted into base calls. The Illumina platform is able to sequence reads up to 300 bp with relatively low error rates. Read length is limited by multiple factors that cause signal decay and dephasing, such as incomplete cleavage of fluorescent labels or terminating moieties. The great majority of the sequencing errors are substitution errors, while insertion/deletion errors are much less common.

The ABI SOLiD is another widely used sequencing platform and has its origins in the system described by Shendure et al. [47] in 2005. The sequencing process used by ABI SOLiD is very similar to the Solexa workflow, however, there are also some differences. First of all, the clonal sequencing features are generated by emulsion PCR, instead of bridge PCR. Second, the SOLiD system uses a di-base sequencing technique in which two nucleotides are read simultaneously at every step of the sequencing process, while the Illumina system reads the DNA sequences directly. Although there are 16 possible pairs of di-bases, the SOLiD system uses only four dyes and so sets of four di-bases are all represented by a single color. As the sequencing machine moves along the read, each base is interrogated twice: first as the right nucleotide of a pair, and then as the left one. In this way, it is possible to derive each subsequent letter if we know the previous one, and if one of the colors in a read is misidentified (e.g. due to a sequencing error), this will change all of the subsequent letters in the translation.

Third Generation NGS Platforms

Sequencing from a single DNA molecule is now called as the "third generation of high throughput NGS technology" [45]. The concept of sequencing-by-synthesis without a prior amplification step, i.e., single molecule sequencing is currently pursued by a number of companies. Unlike the second generation NGS technologies, which rely on PCR to grow clusters of a given DNA template, attaching the clusters of DNA templates to a solid surface that is subsequently imaged as the clusters are sequenced by synthesis in a phased approach, the third generation NGS technologies interrogate single molecules of DNA in a such a way that no synchronization (a limitation of second generation NGS) is required [50], thereby overcoming issues related to the biases introduced by PCR amplification and dephasing. Furthermore, they have the potential to exploit more fully, the high catalytic rates and high processivity of DNA polymerase, or avoid any biology or chemistry altogether to radically increase read length (from tens of bases, to tens of thousands of bases per read) and time to result (from days, to hours, or minutes). Besides this, they may offer the following advantages over second generation NGS technologies: i) higher throughput, ii) faster turnaround time, iii) longer read lengths to enhance de novo assembly and enable direct detection of haplotypes and even whole chromosome phasing, iv) higher consensus accuracy to enable rare variant detection, v) small amounts of starting material, and vi) low cost, where sequencing the human genome at high fold coverage for less than $1,000 is now a reasonable goal for the community. The third generation NGS platforms (Helicos and Pacific Biosciences etc.) are summarized in Fig. 2 [43].

Fig. 2.Advanced technological features of four leading third generation high throughput (HT) NGS platforms [43].

Paired-end and Mate-pair Sequencing

All the sequencing technologies introduced above are able to generate paired-end or mate-pair data. Mate-pairs are created when genomic DNA is fragmented and size-selected inserts are circularized and linked by means of an internal adaptor. After purification, the mate-pairs are generated by sequencing around the adaptor. By contrast, paired-end reads are generated by the fragmentation of genomic DNA into short (＜300 bp) segments, followed by sequencing of both ends of the segment. Although the approaches to obtain mate-pair and pair-end libraries are very different, from a computational perspective, the distinction between matepairs and paired-ends is not crucial: paired reads are two sequences, generated at an approximately known distance from each other in the genome (the insert size). Paired reads are very useful for short-read data analysis: during the alignment process, a large fraction of short reads are difficult to map uniquely to the genome, and the second read of a pair can be used to find the correct location. Moreover, mate-pairs are also typically used to discover structural variants (SVs), that is, regions of the genome that have undergone large-scale mutations such as inversions, insertions, and deletions.

 

Methods for Mapping/Alignment, Assembly and Polymorphism Detection

The NGS platforms generate shorter reads with lower quality, when compared to the Sanger sequencing method. In other words, the sheer volume of NGS data coupled with their relatively short reads and low quality raises the question of how to analyze these data so as to maximize their scientific value. Once sequencing is complete, raw sequence data must undergo several analysis steps. A generalized data analysis pipeline for NGS data includes preprocessing the data to remove adapter sequences and low quality reads, mapping of the data to a reference genome or de novo assembly of the sequence reads, and analysis of the compiled sequence [12]. The sequence analysis can include a wide variety of bioinformatics assessments, including genetic variant calling for detection of SNPs or indels (i.e., the insertion or deletion of bases), detection of novel genes or regulatory elements, and assessment of transcript expression levels. Many free online tools and software packages exist to perform the bioinformatics necessary to successfully analyze sequence data [14]. Although this provides many useful resources, it has delayed or prevented the selection of standard best practice tools for analysis. Any NGS research will require significant computational resources, and a core of bioinformaticians with skills to install, update, and run the latest tools. Furthermore, using NGS platforms can necessitate planning for at least several hundred terabytes of data storage.

Alignment or Mapping

The first important challenge provided by NGS data is the read alignment or mapping problem. When NGS was initially introduced, established alignment tools, suited for the query of a limited number of sequences, were inadequate for NGS data which comprised millions of short reads. This spurred the design of novel alignment algorithms and tools which use heuristic techniques for alignment of millions of short reads within an acceptable time requirement [25]. These tools make the best use of the many advantages specific to each of the NGS platforms such as the short length of SOLiD and Helicos reads, the low indel error rate of Illumina reads and the di-nucleotide encoding of SOLiD reads. These short read alignment tools outperform the performance of traditional alignment programs such as BLAST [20] in terms of speed and accuracy. The degree of confidence can vary as a result of both the reference databases and the shortness of reads. Intrinsically, they must be able to quickly and efficiently align the billions of short reads and allow the alignment of non-unique reads or reads that do not match exactly the reference genome owing to sequencing errors/variations or repetitive element. Representative public short read alignment tools are listed in the table 2. They are able to output alignments in the SAM format [27] that is widely supported by alignment viewers. BWA and Mosaik work well for Sanger and 454 reads, allowing gaps and clipping. Bowtie and MAQ allow base quality scores to be used, improving alignment accuracy. MAQ only does gapped alignment for Illumina paired-end reads. All of the tools reported in the table 2 allow use of paired-end mapping. Paired-end alignment outperforms single-end alignment in terms of both sensitivity and specificity, allowing for a smaller number of wrongly mapped reads [26]. On speed, Bowtie, BWA and SOAP2 align ~7 Gbp against the human genome per CPU day, outperforming the other short read alignment tools.

Table 2.A selection of short reads alignment tools Program The platform compatibility depends on the maximum read length supported by the program [32].

When choosing an alignment tool, one needs to consider some important features including the following: 1) Quality utilization and control―Most alignment software generate the alignment output in the Sequence Alignment Map (SAM) format, with a multitude of supporting downstream analysis tools. Alignment output contains a PHRED based quality score describing the probability of per-base false alignment. These quality scores can be re-assessed using currently available tools [24], 2) Gapped alignment. Alignment tools may or may not use a gap alignment algorithm. When specifically detecting for indels [22], it is highly recommended to choose a tool that implements gapped alignment [26], 3) Mismatches and Gap penalties. Most alignment tools allow the user to set the number of allowed mismatches between the read and a reference location and the scoring scale for gap opening and extension, and 4) Multiple mapping. Usually, a portion of the reads will remain unmapped due to contaminant origin or sequencing errors. More commonly, they will ambiguously map to several different locations (multiple mapping) due to sequence homology and repetitiveness. Of the current approaches for allocation of these multiply mapped reads, one uses probabilistic models such as maximum likelihood to compute the most likely origin of each read.

De novo Assembly

Assembly refers to the process of piecing together short sequences into longer ones. These long sequences, called contigs, are then grouped to form scaffolds for computationally reconstructing a sample's genetic component. When the assembly process is performed with the assistance of a reference genome, it is referred to as mapping assembly; if no reference is available it is called de novo assembly. Tools that allow for the de novo short read assembly are essential when a reference genome does not exist or, in general, when a novel genome assembly is desired. The currently accepted methods most capable of assembling NGS data utilize k-mer de Bruijn graph traversal-based methods, including programs such as AbySS [31], ALLPATHS [13], Edena [21], Velvet [8], SOAPdenovo [51] as well as a number of newer assemblers under development. Even though all these programs are based on the de Bruijn graph data structure, they differ in how they treat errors and if they use read-pair information. To date, de novo assembly of the human genome from high throughput sequence data is able only to reconstruct short DNA regions (contigs), as the presence of repeats makes it difficult or impossible to assemble longer pieces.

It is important to note two things about k-mer based assembly. First, this method reduces the time of assembly, but at the cost of requiring significant RAM which is proportional to the size of the genome(s) being assembled and/or the amount of data, which de facto limits the total size of the genome being assembled. Second, this method is non-deterministic. Because reads are broken down into smaller pieces of defined length (k-mers), reads themselves are no longer the target of assembly, leading to the potential introduction of assembly errors.

Variant Calling

Variant calling refers to the identification of single nucleotide polymorphisms (SNPs), insertions and deletions (indels), copy number variations (CNVs) and other types of structural variations, e.g. inversions, translocations etc, in a sequenced sample [9]. The process is complicated by areas of low coverage, sequencing errors, misalignment caused by either low complexity and repeat regions or adjacent variants and library preparation biases (e.g. PCR duplicates) [36].

SNP and indel identification is a very important task when one deals with resequenced genomes. However, only an handful of tools have been implemented [23, 28, 29, 35, 40] for SNP and small (1-5 bp) indel discovery. The goal of these programs consist in judging the likelihood that a locus is a heterozygous or homozygous variant given the error rates of the platform, the probability of bad mappings, and the amount of coverage. For these reasons, all the available tools for SNP and indel discovery follow two main steps: the first is for data preparation and in the second each nucleotide is called under a Bayesian framework.

In the first step (preparation step) each read is evaluated and filtered. Reads that may map to paralogs or repeat sequences are discarded or considered only if other reads give supporting evidence, quality values are reassigned based on various statistics and lastly a re-alignment step is employed to better align small indels. After the preparation step a Bayesian approach is applied to the filtered data. This approach consists of computing the conditional likelihood of the nucleotides at each position by using the Bayes rule:

The Bayes rule states that the posterior probability P(G|R) of a certain genotype G given the data R can be calculated knowing the prior probability of that genotype and the probability of observing the given data from this genotype P(R|G) (likelihood). Usually, the prior P(G) is calculated as the probability of the variant while the probability of observing the prepared reads P(R|G) is then estimated for each possible donor genotype. The tools that use a Bayesian approach are PolyBayes [35], SOAPsnp [30] and MAQ [29]. Other alternative methods, including machine learning, have been proposed by Malhis et al. [34] and by Hoberman et al. [16]. In machine learning approach, site-specific features are generated from read mappings, and this information is used to train a classifier that is used to score the heterozygosity at each position.

Alignment/Assembly Viewers

The advent of high throughput sequencing technologies has given rise to the need for fast, efficient and user-friendly tools for browsing the resultant assemblies or alignments and the re-sequenced genomes. Tools that allow for the visualization of the alignment or assembly of short read data include EagleView [17], MapView [3], the Text Alignment Viewer of SAMtools [27], MaqView [29], Tablet [37] and IGV (http://www.broadinstitute.org/igv/) by Broad Institute (Table 3). When dealing with NGS data, visualization software is required that takes into account the following challenges: processing quickly and efficiently a huge amount of reads, providing high-quality rendering and navigation of the assembled reads and supporting a widening range of assembly formats. Moreover, the increasing diffusion of NGS technologies requires biologist-friendly and easy-to-use softwares with a user-friendly interface.

Table 3.A list of tools for the visualization of alignments or assemblies of short read data [32]

Discovering Structural Variants

Structural variation was originally defined as insertions, deletions and inversions greater than 1 kb in size [11]. With the sequencing of human genomes now becoming routine, the operational spectrum of structural variants and copy number variants has widened to include much smaller events (for example, those ＞50 bp in length). The challenge now is to discover the full extent of structural variation and to be able to genotype it routinely in order to understand its effects on human disease, complex traits and evolution. The discovery and genotyping of structural variation has been central to understanding these disease associations. Ideally, SV discovery and genotyping requires accurate prediction of three features: copy, content and structure. In practice, this goal has remained elusive because SVs tend to reside within repetitive DNA, which makes their characterization more difficult. SVs vary widely in size and there are numerous classes of structural variation: deletions, translocations, inversions, mobile elements, tandem duplications and novel insertions. Within the past decade, a variety of computational and experimental methods has emerged; typically each focuses on a particular class of structural variation limited by frequency and size range of the events [2].

The first HTS-based approach to detect SVs were based on paired-end read mapping (PEM), which identifies insertions and deletions by comparing the distance between mapped read pairs to the average insert size of the genomic library. Although this method is able to identify deletions smaller than 1 kb with high sensitivity, it does not allow the discovery of insertions larger than the average insert size of the library and the exact borders of SVs in complex genomic regions rich in segmental duplication [6]. In this scenario, a very promising approach for the identification of SVs using HTS technologies consists in measuring the depth of coverage (DOC) of reads aligned to the human reference genome. At present, few computational methods have been developed for the analysis of DOC data: Campbell et al. [4] use the Circular Binary Segmentation algorithm [41] originally developed for genomic hybridization microarray data, Chiang et al. [6] use a local change-point analysis technique, Yoon et al. developed a new statistical method based on significance testing that works on intervals of data points, while Magi et al. [33] developed a novel algorithm, named JointSLM, that allows them to analyze DOC signals from multiple samples simultaneously. Several PEM-based algorithms have been developed for the detection of SVs, including PEMer, VariationHunter, MoDIL and BreakDancer. These tools mainly differ on the variant of signatures they detect and on the clustering procedures.

 

Conclusions

The availability of ultra-deep sequencing of genomic DNA will transform the medical fields, especially in analysis of disease causes and development of new drugs and diagnostics, in the near future. Further, it may become a promising tool in the analysis of chromatin immunoprecipitation coupled to DNA microarray (ChIP-chip) or sequencing (ChIP-Seq), RNA sequencing (RNA-Seq), whole genome genotyping, de novo assembling and re-assembling of genome, genome wide structural variation, mutation detection, detection of inherited disorders and complex human diseases, and personal genomics etc. It is anticipated that the NGS technology will probably be fully adopted for clinical purposes in human medicine in the next decade. With the progress of third NGS platform at tremendous pace, one can hope that the goal of determining a whole chromosome sequence from a single original DNA molecule or genome sequence for less than $1,000 could be feasible soon in the near future.

However, the availability of the sheer volume of sequencing data presents a significant challenge for storage, analyses and data management. While novel tools have been developed specifically for NGS data, ranging from short-read alignment programs to algorithms for the detection of structural variants, the complexity of NGS data has presented difficult challenges and exposed a number of analytical bottlenecks. As these sequencing platforms becomes more commonplace, there is an increasingly need for data specialist to extract biological information from the huge amounts of data produced. Therefore, a key task is to get a clear picture of the bioinformatics tools available for the NGS data analysis. In addition, generation of systematically and syntactically unambiguous nomenclature systems for genomic data across species is a crucial task. Such systems are necessary for adequate handling genetic information in the context of comparative functional genomics.