Optimal spliced alignment of homologous cDNA to a genomic DNA template

MOTIVATION: Supplementary cDNA or EST evidence is often decisive for discriminating between alternative gene predictions derived from computational sequence inspection by any of a number of requisite programs. Without additional experimental effort, this approach must rely on the occurrence of cognate ESTs for the gene under consideration in available, generally incomplete, EST collections for the given species. In some cases, particular exon assignments can be supported by sequence matching even if the cDNA or EST is produced from non-cognate genomic DNA, including different loci of a gene family or homologous loci from different species. However, marginally significant sequence matching alone can also be misleading. We sought to develop an algorithm that would simultaneously score for predicted intrinsic splice site strength and sequence matching between the genomic DNA template and a related cDNA or EST. In this case, weakly predicted splice sites may be chosen for the optimal scoring spliced alignment on the basis of surrounding sequence matching. Strongly predicted splice sites will enter the optimal spliced alignment even without strong sequence matching. RESULTS: We designed a novel algorithm that produces the optimal spliced alignment of a genomic DNA with a cDNA or EST based on scoring for both sequence matching and intrinsic splice site strength. By example, we demonstrate that this combined approach appears to improve gene prediction accuracy compared with current methods that rely only on either search by content and signal or on sequence similarity. AVAILABILITY: The algorithm is available as a C subroutine and is implemented in the SplicePredictor and GeneSeqer programs. The source code is available via anonymous ftp from ftp. zmdb.iastate.edu. Both programs are also implemented as a Web service at http://gremlin1.zool.iastate.edu/cgi-bin/s p.cgiand http://gremlin1.zool.iastate.edu/cgi-bin/g s.cgi, respectively. CONTACT: vbrendel@iastate.edu     

Pro-Frame: similarity-based gene recognition in eukaryotic DNA sequences with errors

Performance of existing algorithms for similarity-based gene recognition in eukaryotes drops when the genomic DNA has been sequenced with errors. A modification of the spliced alignment algorithm allows for gene recognition in sequences with errors, in particular frameshifts. It tolerates up to 5% of sequencing errors without considerable drop of prediction reliability when a sufficiently close homologous protein is available (normalized evolutionary distance similarity score 50% or higher).     

Refined annotation of the Arabidopsis genome by complete expressed sequence tag mapping

Expressed sequence tags (ESTs) currently encompass more entries in the public databases than any other form of sequence data. Thus, EST data sets provide a vast resource for gene identification and expression profiling. We have mapped the complete set of 176,915 publicly available Arabidopsis EST sequences onto the Arabidopsis genome using GeneSeqer, a spliced alignment program incorporating sequence similarity and splice site scoring. About 96% of the available ESTs could be properly aligned with a genomic locus, with the remaining ESTs deriving from organelle genomes and non-Arabidopsis sources or displaying insufficient sequence quality for alignment. The mapping provides verified sets of EST clusters for evaluation of EST clustering programs. Analysis of the spliced alignments suggests corrections to current gene structure annotation and provides examples of alternative and non-canonical pre-mRNA splicing. All results of this study were parsed into a database and are accessible via a flexible Web interface at http://www.plantgdb.org/AtGDB/.     

Homology-based gene structure prediction: simplified matching algorithm using a translated codon (tron) and improved accuracy by allowing for long gaps

MOTIVATION: Locating protein-coding exons (CDSs) on a eukaryotic genomic DNA sequence is the initial and an essential step in predicting the functions of the genes embedded in that part of the genome. Accurate prediction of CDSs may be achieved by directly matching the DNA sequence with a known protein sequence or profile of a homologous family member(s). RESULTS: A new convention for encoding a DNA sequence into a series of 23 possible letters (translated codon or tron code) was devised to improve this type of analysis. Using this convention, a dynamic programming algorithm was developed to align a DNA sequence and a protein sequence or profile so that the spliced and translated sequence optimally matches the reference the same as the standard protein sequence alignment allowing for long gaps. The objective function also takes account of frameshift errors, coding potentials, and translational initiation, termination and splicing signals. This method was tested on Caenorhabditis elegans genes of known structures. The accuracy of prediction measured in terms of a correlation coefficient (CC) was about 95% at the nucleotide level for the 288 genes tested, and 97. 0% for the 170 genes whose product and closest homologue share more than 30% identical amino acids. We also propose a strategy to improve the accuracy of prediction for a set of paralogous genes by means of iterative gene prediction and reconstruction of the reference profile derived from the predicted sequences. AVAILABILITY: The source codes for the program 'aln' written in ANSI-C and the test data will be available via anonymous FTP at ftp.genome.ad.jp/pub/genomenet/saitama-cc. CONTACT: gotoh@cancer-c.pref.saitama.jp     

Performance-Guarantee Gene Predictions via Spliced Alignment

An important and still unsolved problem in gene prediction is designing an algorithm that not only predicts genes but estimates the quality ofindividualpredictions as well. Since experimental biologists areinterested mainly in the reliability of individual predictions (rather than in the average reliability of an algorithm) we attempted to develop a gene recognition algorithm that guarantees a certain quality of predictions. We demonstrate here that the similarity level with a related protein is a reliable quality estimator for thespliced alignmentapproach to gene recognition. We also study the average performance of the spliced alignment algorithm for different targets on a complete set of human genomic sequences with known relatives and demonstrate that the average performance of the method remains high even for very distant targets. Using plant, fungal, and prokaryotic target proteins for recognition of human genes leads to accurate predictions with 95, 93, and 91% correlation coefficient, respectively. For target proteins with similarity score above 60%, not only the average correlation coefficient is very high (97% and up) but also the quality of individual predictions isguaranteedto be at least 82%. It indicates that for this level of similarity the worst case performance of the spliced alignment algorithm is better than the average case performance of many statistical gene recognition methods.     

Analysis of Chlamydomonas reinhardtii genome structure using large-scale sequencing of regions on linkage groups I and III

Chlamydomonas reinhardtii is a unicellular green alga that has been used as a model organism for the study of flagella and basal bodies as well as photosynthesis. This report analyzes finished genomic DNA sequence for 0.5% of the nuclear genome. We have used three gene prediction programs as well as EST and protein homology data to estimate the total number of genes in Chlamydomonas to be between 12,000 and 16,400. Chlamydomonas appears to have many more genes than any other unicellular organism sequenced to date. Twenty-seven percent of the predicted genes have significant identity to both ESTs and to known proteins in other organisms, 32% of the predicted genes have significant identity to ESTs alone, and 14% have significant similarity to known proteins in other organisms. For gene prediction in Chlamydomonas, GreenGenie appeared to have the highest sensitivity and specificity at the exon level, scoring 71% and 82%. respectively. Two new alternative splicing events were predicted by aligning Chlamydomonas ESTs to the genomic sequence. Finally recombination differs between the two sequenced contigs. The 350-Kb of the Linkage group III contig is devoid of recombination, while the Linkage group I contig is 30 map units long over 33-kb.     

Gene structure conservation aids similarity based gene prediction

One of the primary tasks in deciphering the functional contents of a newly sequenced genome is the identification of its protein coding genes. Existing computational methods for gene prediction include ab initio methods which use the DNA sequence itself as the only source of information, comparative methods using multiple genomic sequences, and similarity based methods which employ the cDNA or protein sequences of related genes to aid the gene prediction. We present here an algorithm implemented in a computer program called Projector which combines comparative and similarity approaches. Projector employs similarity information at the genomic DNA level by directly using known genes annotated on one DNA sequence to predict the corresponding related genes on another DNA sequence. It therefore makes explicit use of the conservation of the exon–intron structure between two related genes in addition to the similarity of their encoded amino acid sequences. We evaluate the performance of Projector by comparing it with the program Genewise on a test set of 491 pairs of independently confirmed mouse and human genes. It is more accurate than Genewise for genes whose proteins are <80% identical, and is suitable for use in a combined gene prediction system where other methods identify well conserved and non-conserved genes, and pseudogenes.     

Gene structure prediction from consensus spliced alignment of multiple ESTs matching the same genomic locus

MOTIVATION: Accurate gene structure annotation is a challenging computational problem in genomics. The best results are achieved with spliced alignment of full-length cDNAs or multiple expressed sequence tags (ESTs) with sufficient overlap to cover the entire gene. For most species, cDNA and EST collections are far from comprehensive. We sought to overcome this bottleneck by exploring the possibility of using combined EST resources from fairly diverged species that still share a common gene space. Previous spliced alignment tools were found inadequate for this task because they rely on very high sequence similarity between the ESTs and the genomic DNA. RESULTS: We have developed a computer program, GeneSeqer, which is capable of aligning thousands of ESTs with a long genomic sequence in a reasonable amount of time. The algorithm is uniquely designed to tolerate a high percentage of mismatches and insertions or deletions in the EST relative to the genomic template. This feature allows use of non-cognate ESTs for gene structure prediction, including ESTs derived from duplicated genes and homologous genes from related species. The increased gene prediction sensitivity results in part from novel splice site prediction models that are also available as a stand-alone splice site prediction tool. We assessed GeneSeqer performance relative to a standard Arabidopsis thaliana gene set and demonstrate its utility for plant genome annotation. In particular, we propose that this method provides a timely tool for the annotation of the rice genome, using abundant ESTs from other cereals and plants. AVAILABILITY: The source code is available for download at http://bioinformatics.iastate.edu/bioinformatics2go/gs/download.html. Web servers for Arabidopsis and other plant species are accessible at http://www.plantgdb.org/cgi-bin/AtGeneSeqer.cgi and http://www.plantgdb.org/cgi-bin/GeneSeqer.cgi, respectively. For non-plant species, use http://bioinformatics.iastate.edu/cgi-bin/gs.cgi. The splice site prediction tool (SplicePredictor) is distributed with the GeneSeqer code. A SplicePredictor web server is available at http://bioinformatics.iastate.edu/cgi-bin/sp.cgi     

GeneSeqer@PlantGDB: Gene structure prediction in plant genomes

The GeneSeqer@PlantGDB Web server (http://www.plantgdb.org/cgi-bin/GeneSeqer.cgi) provides a gene structure prediction tool tailored for applications to plant genomic sequences. Predictions are based on spliced alignment with source-native ESTs and full-length cDNAs or non-native probes derived from putative homologous genes. The tool is illustrated with applications to refinement of current gene structure annotation and de novo annotation of draft genomic sequences. The service should facilitate expert annotation as a community effort by providing convenient access to all public plant sequences via the PlantGDB database, a simple four-step protocol for spliced alignment and visually appealing displays of the predicted gene structures in addition to detailed sequence alignments.     

Sequence alignment by cross-correlation.

Many recent advances in biology and medicine have resulted from DNA sequence alignment algorithms and technology. Traditional approaches for the matching of DNA sequences are based either on global alignment schemes or heuristic schemes that seek to approximate global alignment algorithms while providing higher computational efficiency. This report describes an approach using the mathematical operation of cross-correlation to compare sequences. It can be implemented using the fast fourier transform for computational efficiency. The algorithm is summarized and sample applications are given. These include gene sequence alignment in long stretches of genomic DNA, finding sequence similarity in distantly related organisms, demonstrating sequence similarity in the presence of massive (approximately 90%) random point mutations, comparing sequences related by internal rearrangements (tandem repeats) within a gene, and investigating fusion proteins. Application to RNA and protein sequence alignment is also discussed. The method is efficient, sensitive, and robust, being able to find sequence similarities where other alignment algorithms may perform poorly.     

Assembling genes from predicted exons in linear time with dynamic programming.

In a number of programs for gene structure prediction in higher eukaryotic genomic sequences, exon prediction is decoupled from gene assembly: a large pool of candidate exons is predicted and scored from features located in the query DNA sequence, and candidate genes are assembled from such a pool as sequences of nonoverlapping frame-compatible exons. Genes are scored as a function of the scores of the assembled exons, and the highest scoring candidate gene is assumed to be the most likely gene encoded by the query DNA sequence. Considering additive gene scoring functions, currently available algorithms to determine such a highest scoring candidate gene run in time proportional to the square of the number of predicted exons. Here, we present an algorithm whose running time grows only linearly with the size of the set of predicted exons. Polynomial algorithms rely on the fact that, while scanning the set of predicted exons, the highest scoring gene ending in a given exon can be obtained by appending the exon to the highest scoring among the highest scoring genes ending at each compatible preceding exon. The algorithm here relies on the simple fact that such highest scoring gene can be stored and updated. This requires scanning the set of predicted exons simultaneously by increasing acceptor and donor position. On the other hand, the algorithm described here does not assume an underlying gene structure model. Indeed, the definition of valid gene structures is externally defined in the so-called Gene Model. The Gene Model specifies simply which gene features are allowed immediately upstream which other gene features in valid gene structures. This allows for great flexibility in formulating the gene identification problem. In particular it allows for multiple-gene two-strand predictions and for considering gene features other than coding exons (such as promoter elements) in valid gene structures.     

Intron-exon structure and gene copy number of a gene encoding for a membrane-intrinsic light-harvesting polypeptide of the red alga Galdieria sulphuraria

Genes for light-harvesting proteins (lhc genes) of higher plants are well examined. However, little is known about the corresponding genes of algae, although this knowledge might give valuable information about the evolution of photosynthetic antennae. In the case of rhodophytes only two cDNA sequences from a single organism, Porphyridium cruentum, have been published. Here we describe an additional sequence from another species, the thermo-acidophilic red alga Galdieria sulphuraria. For the first time also a genomic sequence for a red algal lhc gene is presented. From a cDNA library of G. sulphuraria we isolated a clone containing an open reading frame for a protein of 302 amino acids with a deduced molecular mass of 33.86kDa. It shares major structural features with eukaryotic light-harvesting polypeptides. A proposed cleavage site between transit peptide and mature protein gives rise to a transit peptide of 119 amino acids and a mature protein of 183 residues. Hydropathy analysis suggests that the mature protein consists of three transmembrane helices. Several amino acid residues supposed to bind chlorophyll a and chlorophyll b in higher plants are conserved. The protein shows up to 69% identity and 81% similarity to the Porphyridium polypeptides in the transmembrane helices 1 and 3. Using oligonucleotides annealing in the regions of the start and stop codons of the gene as primers, a DNA sequence was amplified from nuclear G. sulphuraria DNA by PCR. Compared with the cDNA clone, this sequence contains five additional intervening DNA strings of 50-74bp length. Four of them show typical features of spliceosomal introns with GT-AG borders, and the fifth differs by starting with GC. Three of the supposed introns are located in similar positions as introns of higher plant light-harvesting proteins. Southern blotting and hybridization experiments indicate that G. sulphuraria contains at least three copies of this gene.     

Barley disease resistance gene analogs of the NBS-LRR class: identification and mapping

The majority of verified plant disease resistance genes isolated to date are of the NBS-LRR class, encoding proteins with a predicted nucleotide binding site (NBS) and a leucine-rich repeat (LRR) region. We took advantage of the sequence conservation in the NBS motif to clone, by PCR, gene fragments from barley representing putative disease resistance genes of this class. Over 30 different resistance gene analogs (RGAs) were isolated from the barley cultivar Regatta. These were grouped into 13 classes based on DNA sequence similarity. Actively transcribed genes were identified from all classes but one, and cDNA clones were isolated to derive the complete NBS-LRR protein sequences. Some of the NBS-LRR genes exhibited variation with respect to whether and where particular introns were spliced, as well as frequent premature polyadenylation. DNA sequences related to the majority of the barley RGAs were identified in the recently expanded public rice genomic sequence database, indicating that the rice sequence can be used to extract a large proportion of the RGAs from barley and other cereals. Using a combination of RFLP and PCR marker techniques, representatives of all barley RGA gene classes were mapped in the barley genome, to all chromosomes except 4H. A number of the RGA loci map in the vicinity of known disease resistance loci, and the association between RGA S-120 and the nematode resistance locus Ha2 on chromosome 2H was further tested by co-segregation analysis. Most of the RGA sequences reported here have not been described previously, and represent a useful resource as candidates or molecular markers for disease resistance genes in barley and other cereals.     

Three-dimensional cluster analysis identifies interfaces and functional residue clusters in proteins

Three-dimensional cluster analysis offers a method for the prediction of functional residue clusters in proteins. This method requires a representative structure and a multiple sequence alignment as input data. Individual residues are represented in terms of regional alignments that reflect both their structural environment and their evolutionary variation, as defined by the alignment of homologous sequences. From the overall (global) and the residue-specific (regional) alignments, we calculate the global and regional similarity matrices, containing scores for all pairwise sequence comparisons in the respective alignments. Comparing the matrices yields two scores for each residue. The regional conservation score (C(R)(x)) defines the conservation of each residue x and its neighbors in 3D space relative to the protein as a whole. The similarity deviation score (S(x)) detects residue clusters with sequence similarities that deviate from the similarities suggested by the full-length sequences. We evaluated 3D cluster analysis on a set of 35 families of proteins with available cocrystal structures, showing small ligand interfaces, nucleic acid interfaces and two types of protein-protein interfaces (transient and stable). We present two examples in detail: fructose-1,6-bisphosphate aldolase and the mitogen-activated protein kinase ERK2. We found that the regional conservation score (C(R)(x)) identifies functional residue clusters better than a scoring scheme that does not take 3D information into account. C(R)(x) is particularly useful for the prediction of poorly conserved, transient protein-protein interfaces. Many of the proteins studied contained residue clusters with elevated similarity deviation scores. These residue clusters correlate with specificity-conferring regions: 3D cluster analysis therefore represents an easily applied method for the prediction of functionally relevant spatial clusters of residues in proteins.