- Open Access
COGNATE: comparative gene annotation characterizer
BMC Genomicsvolume 18, Article number: 535 (2017)
The comparison of gene and genome structures across species has the potential to reveal major trends of genome evolution. However, such a comparative approach is currently hampered by a lack of standardization (e.g., Elliott TA, Gregory TR, Philos Trans Royal Soc B: Biol Sci 370:20140331, 2015). For example, testing the hypothesis that the total amount of coding sequences is a reliable measure of potential proteome diversity (Wang M, Kurland CG, Caetano-Anollés G, PNAS 108:11954, 2011) requires the application of standardized definitions of coding sequence and genes to create both comparable and comprehensive data sets and corresponding summary statistics. However, such standard definitions either do not exist or are not consistently applied. These circumstances call for a standard at the descriptive level using a minimum of parameters as well as an undeviating use of standardized terms, and for software that infers the required data under these strict definitions. The acquisition of a comprehensive, descriptive, and standardized set of parameters and summary statistics for genome publications and further analyses can thus greatly benefit from the availability of an easy to use standard tool.
We developed a new open-source command-line tool, COGNATE (Comparative Gene Annotation Characterizer), which uses a given genome assembly and its annotation of protein-coding genes for a detailed description of the respective gene and genome structure parameters. Additionally, we revised the standard definitions of gene and genome structures and provide the definitions used by COGNATE as a working draft suggestion for further reference. Complete parameter lists and summary statistics are inferred using this set of definitions to allow down-stream analyses and to provide an overview of the genome and gene repertoire characteristics. COGNATE is written in Perl and freely available at the ZFMK homepage (https://www.zfmk.de/en/COGNATE) and on github (https://github.com/ZFMK/COGNATE).
The tool COGNATE allows comparing genome assemblies and structural elements on multiples levels (e.g., scaffold or contig sequence, gene). It clearly enhances comparability between analyses. Thus, COGNATE can provide the important standardization of both genome and gene structure parameter disclosure as well as data acquisition for future comparative analyses. With the establishment of comprehensive descriptive standards and the extensive availability of genomes, an encompassing database will become possible.
As more and more sequenced genomes become available, studying the commonalities and differences in the structure of genes and genomes has become an exciting and a rapidly expanding research field. Examples of comparative studies of intron size are those published by Yandell et al. , Moss et al. , and Zimmer et al. , who found that intron length evolution behaves clock-like, that ancient bursts of repetitive elements can be responsible for an unusual intron length distribution, and that there is a trend towards shorter introns in the evolution of land plants, respectively. These studies were restricted to a rather unrepresentative selection of animal, fish, and plants species, respectively, due to the lack of genome sequences. Studies with much larger species numbers and a broader taxonomic coverage are becoming feasible.
Elliott & Gregory  recently published a seminal meta-analysis of the genome and gene summary statistics of animals, land plants, fungi, and ‘protists’, relying on 521 species. The large number of species and genomes considered in their analysis allowed the authors to robustly detect statistical trends in genome evolution, such as a positive correlation between genome size and both gene and intron content, while taking phylogenetic relationships into account. These trends have been previously observed (e.g., [5, 6]), but were based on a much smaller taxonomic sampling. Yet, despite the evidently improved availability of sequenced genomes, Elliott & Gregory  struggled with a lack of standards in the disclosure of genome characteristics when compiling data for their analyses; they evaluated 28 parameters of the genomes of 521 species (see Supplement of ), for which only 48% of all possible values were provided in the publications to the respective genomes (cf. Fig. 2) and thus available for the meta-analysis.
The lack of standardization in the publication of gene structure characteristics is a general problem. Not only are some basic gene content and structure statistics frequently presented in a non-standardized manner, it often remains unclear whether or not terms describing gene structure were consistently applied to achieve comparability between analyses. For example, gene counts may or may not be inferred from tallying all predicted transcripts, thus bearing the risk of including alternative transcripts or isoforms as pseudo-replicates in meta-analyses. Furthermore, GC content may be reckoned without respect to IUPAC base-calling ambiguity in the total sequence lengths, which predicates the resulting value on sequencing and assembly quality. Finally, it can be difficult to trace inconsistencies in the use of terms, like ‘exon’ versus ‘coding sequence (CDS)’ despite existing standard vocabularies like the Sequence Ontology . Clearly, comparability and traceability of published data can greatly benefit from standardized analyses of genome organization and gene structure (see also ).
A partial explanation for the lack of a standardized analysis and presentation of fundamental genomic features referring to protein-coding genes is a lack of software that infers the desired statistics. Available tool suites like BEDtools , genomeTools , AEGeAN,Footnote 1 and gfftoolsFootnote 2 are mostly intended for processing rather than describing annotations. While various programming libraries, such as BioPerlFootnote 3 and SeqAn  provide suitable methods, their usage is demanding to researchers without programming experience and fosters the development of custom scripts by researchers with programming skills. The former likely limits the number of scientists who can infer the desired statistics, while the latter increases the risk of inferring incompatible results due to errors and/or misconceptions in analyses and definitions. Thus, there is a need for easy to use software that provides the facility to examine genome annotations for a wealth of structural features of the protein-coding gene repertoire in a concise way and that provides basic and standardized statistics as well as results suitable for downstream applications.
Here we present the tool COGNATE, a Co mparative G e n e A nno t ation Charact e rizer. It fills the above identified gap of software for structural characterization of the annotated protein-coding gene repertoire of a genome. COGNATE allows a quick and easy extraction of basic genome features and gene repertoire data; it is thus a tool to primarily describe a genome and its annotated protein-coding gene repertoire, which is an essential prerequisite for comparative analyses. Given the ongoing genome sequencing efforts, especially by large consortia like 10 k  and i5k , we see an increasing demand for a standardization of large-scale comparisons of genome and gene structure.
With COGNATE, we promote a tool to simultaneously analyze a given protein-coding gene annotation and the corresponding assembled sequences of a genome, here referred to as scaffold or contig sequence (SCS). An overview of the software’s input, work flow, analyzed parameters, and output is visualized in Fig. 1. A complete list of analyzed parameters is given in Additional file 1, a glossary with the definitions of all terms used in this publication and by COGNATE is provided in Additional file 2.
COGNATE requires as input: (1) a gff file in GFF3 formatFootnote 4 containing the annotation of protein-coding genes; (2) a fasta file, containing the corresponding genomic nucleotide sequences, which are exploited to infer the length, GC content, and amino acid sequences of the assembled SCSs and of the predicted protein-coding genes, respectively. The gene annotation has to include at least the features ‘gene’, ‘mRNA’, and ‘exon’, as provided by, for example, BRAKER1  and MAKER2 . Thus, the analysis of partial and pseudogenes depends on their annotation in the analyzed gff file; non-coding genes (i.e., genes without mRNA) are not considered in the analysis. Further technical requirements are several standard Perl libraries as well as the GAL::Annotation and GAL::List libraries to allow gff-handling. The latter two libraries are available from the Sequence Ontology ProjectFootnote 5 and are also included in the COGNATE software package. COGNATE is written in Perl and has been tested under Ubuntu 12.04 and 14.04. COGNATE analyzes one genome at a time. Providing multiple genomes (i.e., a batch) for serial processing is possible with a special input file (see README, Additional file 4). Serial, single-threaded processing leads to a linear relationship of processed genomes and required time. As a gauge, the analysis of the latest Apis mellifera gene set (see Results and Discussion), which has a genome size of 250.3 Mb and 10,733 annotated protein-coding genes, takes with COGNATE up to 4 h, using up to 600 MiB RAM. For comparison, COGNATE requires a very similar amount of time for the analysis of the gene setFootnote 6 of Ixodes scapularis (genome size: 1765.4 Mb, 20,467 annotated protein-coding genes). A benchmark comparison of COGNATE to other software, such as genomeTools , AEGeAN1, or gfftools2, is not meaningful due to major differences between these software packages in focus and aim. At the moment, no tool yields the wide array of metrics that COGNATE delivers by default.
COGNATE infers the following major metrics (for a full list of the 296 parameters, see Additional file 1):
summary counts of the analyzed features, including L90pcGFootnote 7, i.e., the number of SCSs needed to cover 90% of all annotated protein-coding genes;
strandedness of transcripts and features (CDSs, exons, and introns);
lengths and length statistics (nucleotide/amino acid sequences), including N50/L50, 75/L75, N90/L90;
intron length distribution ;
percental GC content statistics in two different ways, namely
using a calculation that explicitly considers IUPAC ambiguity codes (G, C, S per total length excluding N, R, Y, K, M, B, D, H, V);
using the previously prevailing calculation of GC per total length, which is inappropriate for genome comparisons due to its dependence on assembly quality;
statistics of CpG dinucleotide depletion (CpG observed/expected), normalized by C and G content of the respective region ;
density statistics (ratio of the length of a feature covered by another, number-wise);
coverage statistics (ratio of the length of a feature covered by another, length-wise).
In summary, the output parameters can be classified as computations of the eight above major metrics or feature types, some with child types (e.g., added length), of six structural entities (e.g., assembly/annotation, SCSs, introns). In other words, parameters are inferred on several levels. For example, the total count of CDSs in analyzed transcripts is given for the entire assembly as well as on a per transcript basis. For the latter, COGNATE also calculates the mean and median count of CDSs per transcript as well as the mean/median of these medians over all transcripts. As another example, the intron density of a gene is calculated as the total number of introns divided by the length of the gene (i.e., genomic length of the transcript, including introns and exons) and also given as mean/median intron density per gene over the whole annotation. For each gene, only one representative (optionally the longest [default], shortest, or median-length) transcript is evaluated. The analysis is independent of homology hypotheses (i.e., not limited to gene families), thus comprising information on a genome’s entire annotated protein-coding gene repertoire.
As output, COGNATE provides various result tables in TSV format:
a concise overview (summary) of measured variables;
lists of all measured variables referring to features of a given SCS, transcript, or individual CDSs, exons, or introns, respectively;
‘batch’ files, which contain one line of summary statistics per analyzed genome. There are individual files for general genome data and means and medians of SCS and transcript data, respectively;
a component size overview (i.e., the added length [in bp] of all coding and intron sequences, respectively), which offers a basis for a comparison of these values with statistics of other genomic features inferred with other tools, for example non-coding elements;
All above specified files (except the one providing an overview) facilitate tests for correlations between parameters within and among genomes. The output files are formatted specifically to allow easy import in statistical software, such as R  and SPSS . COGNATE also provides a fasta file (‘analyzed_transcripts’) containing the predicted amino acid sequences inferred from the CDSs of the one analyzed transcript per gene. This file can be used, for example, as input for BUSCO  to test for the completeness of the gene set, which is facilitated by the ready-made bash commands supplied in the ‘bash commands’ text file. The generation of all output files can be controlled directly by the user.
The output of COGNATE can be used in manifold analyses, ranging from a descriptive characterization to an in-depth comparative analysis of gene organization across multiple genomes. This is further exemplified in the discussion.
Results and discussion
It is an essential feature of COGNATE to provide not only descriptive statistics but also the complete primary data, since “an over-reliance on simple summary statistics […] can obscure real biological trends and differences” (, p. 1191). Apart from other already mentioned potential applications, COGNATE output can be used to study the variability of gene structure within a genome and to compare it with that in other genomes. In such an instance, the list of transcript features can be exploited to analyze the range of exon lengths, intron lengths, and their distribution over genes of a certain GC content. Another example would be a comparison of GC content in coding and non-coding regions of genes across a genome. Having the characteristics of a gene repertoire at hand, they can be compared to those of other species and used in phylogenomic analyses (e.g., ). COGNATE results can also serve as a starting point to find genes of interest and relate them to functions, e.g., looking for very long or short genes or investigating genes containing exactly two CDSs. Hypotheses like ‘Flying birds have shorter introns than birds of non-volant sister lineages due to energetic demands of powered flight’ , ‘Evolutionary changes in intron lengths correlate with co-expression of genes’ , or ‘Strategies of splice-site recognition are influenced by differences in GC content between exons and introns’  could thereby be tested in more detail. Thus, COGNATE provides data to facilitate downstream analyses, and in addition, provides summary statistics that can help standardizing genome parameter disclosure.
Missing standardization in comparative genomics can easily lead to problems in meta-analyses and consequently result in biased conclusions. As Elliott & Gregory  noted during their tremendous effort of data compilation, there are problems of standardization in terms of parameter listing and source disclosure as well as of definitions of descriptive terms. Some of these subtle and sometimes deemphasized problems are elucidated here in more detail to raise and sustain the awareness for them.
One problem in compiling data for meta-analyses are missing values. The data matrix compiled by Elliott & Gregory (Supplement of Footnote 8) contains overall 52% missing values due to incomplete data disclosure by publications or missing entries in databases. This lack of data introduces a potential bias in correlative analyses of genome structures, which has not been systematically investigated. Thus, without in-depth parameter disclosure, the enormous effort of collecting data from open sources for genome and gene structure comparison potentially yields unreliable results. The general distribution of missing data in the matrix compiled by Elliott & Gregory  is noteworthy in that the GC content is almost always given while values related to gene structure including intron size values are missing for half of the genomes in the data matrix (see Fig. 2). It is surprising to find that for 38% of the genomes in their dataset no assembly genome size was included in the original publications or databases. To further illustrate the problem of missing data in comparative genomics, we analyzed the genome (version 4.5, downloaded 31 August 2015, from NCBIFootnote 9) and latest protein-coding gene annotation (release 103, downloaded 20 March 2017 from NCBIFootnote 10) of Apis mellifera. Compared to the 144 values recorded by COGNATE that can readily be given as a single number, the publications covering the official gene sets 1  and 3.2  offer only eight and nine comparable values, respectively; NCBI offers a report siteFootnote 11 for the most recent annotation release (103), where we found 14 comparable values (Additional file 1, sheet 2). The obtained values differ on a small scale (for example, the count of protein-coding genes differs by 5 for a total of circa 10,730), most likely due to the different annotation versions or deviating definitions. Generally, COGNATE can help to mitigate the problem of missing values by easing their acquisition and has the benefit of providing tractable values with a transparent method.
Problems of fuzzy terminology become apparent when, for example, the coding amount (i.e., the total length of protein-coding sequences within a genome) is given in exonic megabases (Mb) (Fig. 2; ). Given the functional and structural similarity of exons and CDSs and their often complete overlap in automated annotations, it is an understandable, yet potentially misleading lack of differentiation. In contrast to CDSs, annotated exons can include untranslated regions (UTRs) and stop codons; not every exon is a coding sequence . Most of the automated annotations do not include UTRs, which are difficult to delineate de novo (e.g., [27, 28]); nevertheless, a future project is to include the analysis of UTR annotations in COGNATE. Thus, in this instance, it remains unclear in which form exons and CDSs were evaluated and contributed to a summary statistic. With the above example, we are illustrating why we stress the importance of clear definitions and applications of these to genome and gene structure characterizations. Accordingly, COGNATE differentiates between CDSs and exons, but it can only be as accurate as the given annotation. For a complete list of our definitions, compared to Sequence Ontology terms Footnote 12, see the glossary in Additional file 2. The problems of defining a universally needed term such as ‘gene’ (described in ) as well as the various ways and needs of gene annotation  render the ongoing efforts of finding precise and useful definitions both essential and exacting.
Another problem of terminological and methodological nature is the widespread use of means as descriptive summary statistic. Since many gene structure features are not normally distributed within a genome, the mean is an inappropriate summary statistic of these features. Yet, in many investigations, only the mean is calculated as a summary statistic of gene structure features (see  as well as the publications cited therein). Doing so can bias analyses and severely mislead comparisons between genomes, especially when one is represented by a mean, the other by a median. To illustrate this, we used results of COGNATE from analyzing the latest gene set of Apis mellifera (see above) and compared the obtained values of mean and median of exon size and intron size per transcript, respectively (Fig. 3, data in Additional file 3). In normally distributed data, means and medians are expected to be (nearly) identical, which is clearly not the case in A. mellifera. COGNATE calculates both means and medians for a wealth of parameters.
A third example of unclear usage of terms relates to the evaluation of intron density. The two above evaluated parameters — exon size and intron size per transcript — together with intron density per transcript can be understood as a proxy for gene structure, as demonstrated by Yandell et al. , and are thus of great interest in structural gene characterization. Note however that intron density as calculated by Yandell et al.  relates to protein length (i.e., count of introns/protein length). We advocate (and implemented in COGNATE) the relation of intron density to gene length as described above, since proteins as well as mature mRNAs are spliced and thus intron-free.
Aside from reporting important insights, Elliott & Gregory  advocated the need for standardization in large-scale comparisons of genomes. The inevitable problems of analyzing datasets with missing data could, in the future, be extenuated by a common, comprehensive set of basic parameters published together with genomic data. When publishing a genome and its annotation of protein-coding genes, it would be most beneficial to attach the complete set of COGNATE results to it to avoid problems resulting from changing versions of genomes and/or annotations. A set of standard metrics to advance standardization of parameter publishing was proposed by Elliott & Gregory , including “details of base pair composition, gene number, intron number and size, total repeat content, and TE abundance, diversity and activity” (, p. 8). Many other parameters can and should be used to describe the features of a genome completely, most of which go beyond the scope of COGNATE (e.g., properties of repetitive elements). Regarding protein-coding genes, we suggest to cover the descriptive parameters more broadly and to provide the following parameters as a minimum:
assembly size (i.e., total added length of all SCSs, with and without Ns),
assembly GC content (with and without ambiguity),
median transcript length (tallying one representative transcript per gene),
median CDS length,
median CDS count per transcript (i.e., density),
median CDS length per gene (i.e., coverage),
coding amount (i.e., total added length of all CDSs),
median intron length,
median intron count per transcript (i.e., density),
median intron length per gene (i.e., coverage),
intron amount (i.e., total added length of all introns).
Following the establishment of standard parameters of gene model properties and the institution of a standard tool to acquire these, the next desirable step is the constitution of a “curated, user-friendly, open-access database [to] make this information accessible and usable in large-scale comparative analyses” (, p. 8).
Finally, we would like to draw the readers’ awareness also to a frequently encountered problem in comparative genomics: the source of primary sequence data or the version of gene annotations are often not clearly stated, which hampers reproducibility of the published analyses. Therefore, we emphasize the need for disclosing used databases, genome versions, and other source information in combination with data and results.
Comparative meta-analyses of gene and genome characteristics, testing, for example, whether potential proteome diversity is reliably reflected by the total amount of coding sequences , rely on descriptive statistics of primary genome sequences and gene annotations. However, comprehensive standard statistics of genome organization and gene structure have not been fully or consistently defined with the effect that they are inconsistently collected or often incomplete. Due to this problem, comparative meta-analyses of gene and genome characteristics can be severely handicapped and are potentially unreliable. Obviously, this problem can be solved with the routine application of standard tools. The here presented software COGNATE allows effortless and flexible parameter disclosure as well as genome comparisons within its designated scope. Its merits include the comprehensive evaluation of an extensive set of standard and non-standard parameters of protein-coding genes, the provision of both primary data and summary statistics, and the use of explicit term definitions. COGNATE was developed in the hope to further promote and ease comparative studies, which should eventually yield insights into the evolution of genomes and gene repertoires.
Availability and requirements
COGNATE is provided as a package, including source code, helper scripts (e.g., to check the presence of required Perl libraries), example data, GAL libraries, and manual at the ZFMK website and together with this publication as Additional file 4.
Project name: COGNATE
Operating system(s): platform independent
Programming language: Perl
Other requirements: GAL libraries (included)
License: GNU GPLv3
The datasets analyzed during the current study are available in the NCBI RefSeq repositories7,8 and from the supplement12 of .
Standage DS. AEGeAn: an integrated toolkit for analysis and evaluation of annotated genomes. 2010–2015. http://standage.github.io/AEGeAn. Last accessed 20 March 2017.
GitHub: Holmes I. gfftools. 2011. https://github.com/ihh/gfftools. Last accessed 20 March 2017.
The BioPerl Project. 2016. http://bioperl.org. Last accessed 20 March 2017.
The Generic Model Organism Database: GFF format definition. 2016. http://gmod.org/wiki/GFF3. Last accessed 20 March 2017.
The Genome Annotation Library. 2016. http://www.sequenceontology.org/software/GAL.html. Last accessed 20 March 2017.
Data of Ixodes scapularis: NCBI: FTP directory of the Ixodes scapularis genome version JCVI_ISG_i3_1.0 and the corresponding protein-coding gene annotation (NCBI RefSeq). 2017. ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/000/208/615/GCF_000208615.1_JCVI_ISG_i3_1.0/. Last accessed 20 March 2017.
L90pcG, the count of SCSs necessary to cover 90% of the annotated protein-coding genes in an assembly, is here, to our knowledge, explicitly termed for the first time. Similar metrics have been described in other publications, for example, the “number of whole genome CARs [Contiguous Ancestral Regions] that cover 90% of one-to-one orthologous families” (, SOM, page 57). Although the notation of L for a number (instead of a length) appears to be counter-intuitive, we deliberately decided to follow the already established convention of N50 and L50, with N50 designating “maximum length L such that 50% of all nucleotides lie in contigs (or scaffolds) of size at least L” , and L50 designating the “number of sequences evaluated at the point when the sum length exceeds 50% of the assembly size” (Bradnam K. ACGT. 2015. http://www.acgt.me/blog/2015/6/11/l50-vs-n50-thats-another-fine-mess-that-bioinformatics-got-us-into. Last accessed 23 May 2017).
Elliott TA, Gregory TR. Supplement 1 – Genome data used in the analyses. 2015. http://rstb.royalsocietypublishing.org/highwire/filestream/32237/field_highwire_adjunct_files/0 /rstb20140331supp1.xlsx. Last accessed 20 March 2017.
NCBI: FTP directory of the Apis mellifera genome version 4.5 (NCBI RefSeq). 2016. ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCF /000/002/195/GCF_000002195.4_Amel_4.5/. Genome file downloaded 31 August 2015. Last accessed 20 March 2017.
NCBI: FTP directory of the Apis mellifera annotation release 103. 2017. ftp://ftp.ncbi.nlm.nih.gov/genomes/Apis_mellifera/GFF/. Annotation file downloaded 20 March 2017. Last accessed 20 March 2017.
NCBI: NCBI Apis mellifera Annotation Release 103 report site. 2016. https://www.ncbi.nlm.nih.gov/genome/annotation_euk/Apis_mellifera/103/. Last accessed 20 March 2017.
The Sequence Ontology. 2016. http://www.sequenceontology.org. Last accessed 20 March 2017.
- CpG o/e:
Cytosine-guanine dinucleotides observed/expected
Megabases (1 Mb = 1000 basepairs [bp])
Megabinary byte (1 MiB = 1,048,576 bytes)
Random access memory (‘working memory’ of a computer)
Scaffold or contig sequence
Yandell M, Mungall CJ, Smith C, Prochnik S, Kaminker J, Hartzell G, et al. Large-scale trends in the evolution of Gene structures within 11 animal genomes. PLoS Comput Biol. 2006;2:e15.
Moss SP, Joyce DA, Humphries S, Tindall KJ, Lunt DH. Comparative analysis of Teleost genome sequences reveals an ancient Intron size expansion in the Zebrafish lineage. Genome Biol Evol. 2011;3:1187–96.
Zimmer AD, Lang D, Buchta K, Rombauts S, Nishiyama T, Hasebe M, et al. Reannotation and extended community resources for the genome of the non-seed plant Physcomitrella patens provide insights into the evolution of plant gene structures and functions. BMC Genomics. 2013;14:498.
Elliott TA, Gregory TR. What’s in a genome? The C-value enigma and the evolution of eukaryotic genome content. Philos Trans Royal Soc B: Biol Sci. 2015;370:20140331.
Hou Y, Lin S. Distinct Gene number-genome size relationships for eukaryotes and non-eukaryotes: Gene content estimation for Dinoflagellate genomes. PLoS One. 2009;4:e6978.
Zhang Q, Edwards SV. The evolution of Intron size in amniotes: a role for powered flight? Genome Biol Evol. 2012;4:1033–43.
Eilbeck K, Lewis SE, Mungall CJ, Yandell M, Stein L, Durbin R, et al. The sequence Ontology: a tool for the unification of genome annotations. Genome Biol. 2005;6:R44.
Gregory TR. Synergy between sequence and size in large-scale genomics. Nat Rev Genet. 2005;6:699–708.
Quinlan AR. In: Bateman A, Pearson WR, Stein LD, Stormo GD, Yates JR, editors. BEDTools: the Swiss-Army tool for genome feature analysis: BEDTools: the Swiss-Army tool for genome feature analysis, Current protocols in bioinformatics [internet]. Hoboken: Wiley; 2014. [cited 2016 Sep 2]. p. 11.12.1-11.12.34. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4213956/.
Gremme G, Steinbiss S, Kurtz S. GenomeTools: a comprehensive software library for efficient processing of structured genome annotations. IEEE/ACM Trans Comput Biol Bioinforma. 2013;10:645–56.
Döring A, Weese D, Rausch T, Reinert K. SeqAn an efficient, generic C++ library for sequence analysis. BMC Bioinformatics. 2008;9:11.
Koepfli K-P. Paten B, the genome 10K Community of Scientists, O’Brien SJ. The genome 10K project: a way forward. Annu Rev Animal Biosciences. 2015;3:57–111.
Consortium i5K. The i5K initiative: advancing arthropod genomics for knowledge, human health, agriculture, and the environment. J Hered. 2013;104:595–600.
Hoff KJ, Lange S, Lomsadze A, Borodovsky M, Stanke M. BRAKER1: Unsupervised RNA-Seq-Based Genome Annotation with GeneMark-ET and AUGUSTUS. Bioinformatics. 2016;32:767-9.
Holt C, Yandell M. MAKER2: an annotation pipeline and genome-database management tool for second-generation genome projects. BMC Bioinform. 2011;12:491.
Roy SW, Penny D. Intron length distributions and gene prediction. Nucl. Acids Res. 2007;35:4737–42.
Elango N, Hunt BG, Goodisman MAD, Yi SV. DNA methylation is widespread and associated with differential gene expression in castes of the honeybee, Apis mellifera. PNAS. 2009;106:11206–11.
Core Team R. R: a language and environment for statistical computing [internet]. Vienna: R Foundation for Statistical Computing; 2015. Available from: http://www.R-project.org
IBM SPSS Statistics for Windows. Armonk, NY: IBM Corp.; (Released 2013).
Simão FA, Waterhouse RM, Ioannidis P, Kriventseva EV, Zdobnov EM. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics. 2015;31:3210–2.
Niehuis O, Hartig G, Grath S, Pohl H, Lehmann J, Tafer H, et al. Genomic and morphological evidence converge to resolve the enigma of Strepsiptera. Curr Biol. 2012;22:1309–13. (Corrigendum in Current Biology 23:1388)
Keane PA, Seoighe C. Intron length Coevolution across mammalian genomes. Mol Biol Evol. 2016;33:2682–91.
Amit M, Donyo M, Hollander D, Goren A, Kim E, Gelfman S, et al. Differential GC content between Exons and Introns establishes distinct strategies of splice-site recognition. Cell Rep. 2012;1:543–56.
The Honeybee Genome Sequencing Consortium. Insights into social insects from the genome of the honeybee Apis mellifera. Nature. 2006;443:931–49.
Elsik CG, Worley KC, Bennett AK, Beye M, Camara F, Childers CP, et al. Finding the missing honey bee genes: lessons learned from a genome upgrade. BMC Genomics. 2014;15:86.
Zhang MQ. Computational prediction of eukaryotic protein-coding genes. Nat Rev Genet. 2002;3:698–709.
Griffith OL, Montgomery SB, Bernier B, Chu B, Kasaian K, Aerts S, et al. ORegAnno: an open-access community-driven resource for regulatory annotation. Nucl. Acids Res. 2008;36:D107–13.
Mignone F, Grillo G, Licciulli F, Iacono M, Liuni S, Kersey PJ, et al. UTRdb and UTRsite: a collection of sequences and regulatory motifs of the untranslated regions of eukaryotic mRNAs. Nucl Acids Res. 2005;33:D141–6.
Gerstein MB, Bruce C, Rozowsky JS, Zheng D, Du J, Korbel JO, et al. What is a gene, post-ENCODE? History and updated definition. Genome Res. 2007;17:669–81.
Mudge JM, Harrow J. The state of play in higher eukaryote gene annotation. Nat Rev Genet. 2016;17:758–72.
Wang M, Kurland CG, Caetano-Anollés G. Reductive evolution of proteomes and protein structures. PNAS. 2011;108:11954–8.
We thank Malte Petersen, Jan Philip Oeyen, and Tanja Ziesmann for beta-testing COGNATE and Joshua D Gibson as well as three anonymous reviewers for helpful feedback on the manuscript. We further acknowledge the students of the Leibniz Graduate School on Genomic Biodiversity Research, the i5K community, and especially Anna Childers, for valuable feedback on ideas put forth in this study. JW thanks Barry Moore for help implementing GAL in the software package COGNATE. Finally, BM, JW and ON thank the German Research Foundation for support of this study and acknowledge the Leibniz Association for funding the Graduate School on Genomic Biodiversity Research.
This study was supported by the Leibniz Graduate School on Genomic Biodiversity Research and by the German Research Foundation (MI 649/16–1, NI-1387/3–1). The funding agencies did not influence the design of the study, the collection, analysis, and interpretation of data, or the manuscript writing.
During the becoming of this study, JW was a PhD candidate in entomological phylogenomics. Amidst the quest to tackle insect genomes and evaluate their commonalities and differences, the lack of a very basic tool became apparent and pressing. Thus, COGNATE was written to establish such a tool and to subsequently provide it to the community.
Ethics approval and consent to participate
Consent for publication
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Parameter table. List of parameters recorded by COGNATE. The first sheet of this table contains all 296 parameters evaluated by COGNATE, including the output file in which to find them and explanatory comments. Sorting for parameters, the individual feature (‘of’) or the feature location (‘per’), and files allows to quickly find a parameter of interest. The second sheet contains a comparison of the values recorded by COGNATE when analyzing the latest annotation of the Apis mellifera genome (genome version 4.58, annotation release 1039) to those values given in the publications of the official gene sets version 1  and 3.2  and in the annotation report by NCBI11. As an addition, we included the results of GenomeTools’ ‘gt stat’ command applied to the annotation release 103 GFF file for comparison. (XLSX 43 kb)
Definition table. Glossary and definitions used by COGNATE. This document contains the definitions used by COGNATE and in this manuscript for structural entities and measured parameters. Where available, we added matching Sequence Ontology terms. (PDF 110 kb)
Result table. COGNATE results of analyzing exon and intron lengths of Apis mellifera. This data sheet contains the mean and median lengths of exons and introns, which are part of the 10,733 transcripts analyzed by COGNATE (default run, i.e., using the longest of each gene’s alternative transcripts). In total, 76,276 exons and 65,543 introns were taken into account. The data is visualized in Fig. 2. (XLSX 225 kb)