Expressed sequence tags of Chinese cabbage flower bud cDNA.

We randomly selected and partially sequenced cDNA clones from a library of Chinese cabbage (Brassica campestris L. ssp. pekinensis) flower bud cDNAs. Out of 1216 expressed sequence tags (ESTs), 904 cDNA clones were unique or nonredundant. Five hundred eighty-eight clones (48.4%) had sequence homology to functionally defined genes at the peptide level. Only 5 clones encoded known flower-specific proteins. Among the cDNAs with no similarity to known protein sequences (628), 184 clones had significant similarity to nucleotide sequences registered in the databases. Among these 184 clones, 142 exhibited similarities at the nucleotide level only with plant ESTs. Also, sequence similarities were evident between these 142 ESTs and their matching ESTs when compared using the deduced amino acid sequences. Therefore, it is possible that the anonymous ESTs encode plant-specific ubiquitous proteins. Our extensive EST analysis of genes expressed in floral organs not only contributes to the understanding of the dynamics of genome expression patterns in floral organs but also adds data to the repertoire of all genomic genes.     

Intrinsic and extrinsic approaches for detecting genes in a bacterial genome.

The unannotated regions of the Escherichia coli genome DNA sequence from the EcoSeq6 database, totaling 1,278 'intergenic' sequences of the combined length of 359,279 basepairs, were analyzed using computer-assisted methods with the aim of identifying putative unknown genes. The proposed strategy for finding new genes includes two key elements: i) prediction of expressed open reading frames (ORFs) using the GeneMark method based on Markov chain models for coding and non-coding regions of Escherichia coli DNA, and ii) search for protein sequence similarities using programs based on the BLAST algorithm and programs for motif identification. A total of 354 putative expressed ORFs were predicted by GeneMark. Using the BLASTX and TBLASTN programs, it was shown that 208 ORFs located in the unannotated regions of the E. coli chromosome are significantly similar to other protein sequences. Identification of 182 ORFs as probable genes was supported by GeneMark and BLAST, comprising 51.4% of the GeneMark 'hits' and 87.5% of the BLAST 'hits'. 73 putative new genes, comprising 20.6% of the GeneMark predictions, belong to ancient conserved protein families that include both eubacterial and eukaryotic members. This value is close to the overall proportion of highly conserved sequences among eubacterial proteins, indicating that the majority of the putative expressed ORFs that are predicted by GeneMark, but have no significant BLAST hits, nevertheless are likely to be real genes. The majority of the putative genes identified by BLAST search have been described since the release of the EcoSeq6 database, but about 70 genes have not been detected so far. Among these new identifications are genes encoding proteins with a variety of predicted functions including dehydrogenases, kinases, several other metabolic enzymes, ATPases, rRNA methyltransferases, membrane proteins, and different types of regulatory proteins.     

The Sorcerer II Global Ocean Sampling Expedition: Expanding the Universe of Protein Families

Metagenomics projects based on shotgun sequencing of populations of micro-organisms yield insight into protein families. We used sequence similarity clustering to explore proteins with a comprehensive dataset consisting of sequences from available databases together with 6.12 million proteins predicted from an assembly of 7.7 million Global Ocean Sampling (GOS) sequences. The GOS dataset covers nearly all known prokaryotic protein families. A total of 3,995 medium- and large-sized clusters consisting of only GOS sequences are identified, out of which 1,700 have no detectable homology to known families. The GOS-only clusters contain a higher than expected proportion of sequences of viral origin, thus reflecting a poor sampling of viral diversity until now. Protein domain distributions in the GOS dataset and current protein databases show distinct biases. Several protein domains that were previously categorized as kingdom specific are shown to have GOS examples in other kingdoms. About 6,000 sequences (ORFans) from the literature that heretofore lacked similarity to known proteins have matches in the GOS data. The GOS dataset is also used to improve remote homology detection. Overall, besides nearly doubling the number of current proteins, the predicted GOS proteins also add a great deal of diversity to known protein families and shed light on their evolution. These observations are illustrated using several protein families, including phosphatases, proteases, ultraviolet-irradiation DNA damage repair enzymes, glutamine synthetase, and RuBisCO. The diversity added by GOS data has implications for choosing targets for experimental structure characterization as part of structural genomics efforts. Our analysis indicates that new families are being discovered at a rate that is linear or almost linear with the addition of new sequences, implying that we are still far from discovering all protein families in nature.     

Oceanic Metagenomics: The Sorcerer II Global Ocean Sampling Expedition: Expanding the Universe of Protein Families

Metagenomics projects based on shotgun sequencing of populations of micro-organisms yield insight into protein families. We used sequence similarity clustering to explore proteins with a comprehensive dataset consisting of sequences from available databases together with 6.12 million proteins predicted from an assembly of 7.7 million Global Ocean Sampling (GOS) sequences. The GOS dataset covers nearly all known prokaryotic protein families. A total of 3,995 medium- and large-sized clusters consisting of only GOS sequences are identified, out of which 1,700 have no detectable homology to known families. The GOS-only clusters contain a higher than expected proportion of sequences of viral origin, thus reflecting a poor sampling of viral diversity until now. Protein domain distributions in the GOS dataset and current protein databases show distinct biases. Several protein domains that were previously categorized as kingdom specific are shown to have GOS examples in other kingdoms. About 6,000 sequences (ORFans) from the literature that heretofore lacked similarity to known proteins have matches in the GOS data. The GOS dataset is also used to improve remote homology detection. Overall, besides nearly doubling the number of current proteins, the predicted GOS proteins also add a great deal of diversity to known protein families and shed light on their evolution. These observations are illustrated using several protein families, including phosphatases, proteases, ultraviolet-irradiation DNA damage repair enzymes, glutamine synthetase, and RuBisCO. The diversity added by GOS data has implications for choosing targets for experimental structure characterization as part of structural genomics efforts. Our analysis indicates that new families are being discovered at a rate that is linear or almost linear with the addition of new sequences, implying that we are still far from discovering all protein families in nature.     

The Sorcerer II Global Ocean Sampling Expedition: Expanding the Universe of Protein Families

Metagenomics projects based on shotgun sequencing of populations of micro-organisms yield insight into protein families. We used sequence similarity clustering to explore proteins with a comprehensive dataset consisting of sequences from available databases together with 6.12 million proteins predicted from an assembly of 7.7 million Global Ocean Sampling (GOS) sequences. The GOS dataset covers nearly all known prokaryotic protein families. A total of 3,995 medium- and large-sized clusters consisting of only GOS sequences are identified, out of which 1,700 have no detectable homology to known families. The GOS-only clusters contain a higher than expected proportion of sequences of viral origin, thus reflecting a poor sampling of viral diversity until now. Protein domain distributions in the GOS dataset and current protein databases show distinct biases. Several protein domains that were previously categorized as kingdom specific are shown to have GOS examples in other kingdoms. About 6,000 sequences (ORFans) from the literature that heretofore lacked similarity to known proteins have matches in the GOS data. The GOS dataset is also used to improve remote homology detection. Overall, besides nearly doubling the number of current proteins, the predicted GOS proteins also add a great deal of diversity to known protein families and shed light on their evolution. These observations are illustrated using several protein families, including phosphatases, proteases, ultraviolet-irradiation DNA damage repair enzymes, glutamine synthetase, and RuBisCO. The diversity added by GOS data has implications for choosing targets for experimental structure characterization as part of structural genomics efforts. Our analysis indicates that new families are being discovered at a rate that is linear or almost linear with the addition of new sequences, implying that we are still far from discovering all protein families in nature.     

Sequence Analysis of a 25-kb Segment in the 17{degrees}-19{degrees} Region of the Bacillus subtilis Chromosome Containing ada Locus

As a part of the Bacillus subtilis genome sequencing project,we have determined a 25-kb sequence covering the 17°–19°region. This region contains 26 complete open reading frames(ORFs) including the alkA and adaA/B operon, which encode genesfor adaptive response to DNA alkylation. A homology search forthe newly identified 21 ORFs revealed that 4 of them exhibita significant similarity to known proteins, e.g., methicillin-resistantStaphylococcus aureus (MRSA) protein homolog, proteins involvedin chloramphenicol resistance, glucosamine synthase and an ABCtransporter protein. The remaining 17 ORFs did not show anysignificant sequence similarities to known gene products inthe database.     

Pfam 10 years on: 10,000 families and still growing

Classifications of proteins into groups of related sequences are in some respects like a periodic table for biology, allowing us to understand the underlying molecular biology of any organism. Pfam is a large collection of protein domains and families. Its scientific goal is to provide a complete and accurate classification of protein families and domains. The next release of the database will contain over 10,000 entries, which leads us to reflect on how far we are from completing this work. Currently Pfam matches 72% of known protein sequences, but for proteins with known structure Pfam matches 95%, which we believe represents the likely upper bound. Based on our analysis a further 28,000 families would be required to achieve this level of coverage for the current sequence database. We also show that as more sequences are added to the sequence databases the fraction of sequences that Pfam matches is reduced, suggesting that continued addition of new families is essential to maintain its relevance.     

Analysis of expressed sequence tags from Trypanosoma cruzi amastigotes

A total of 880 expressed sequence tags (EST) originated from clones randomly selected from a Trypanosoma cruzi amastigote cDNA library have been analyzed. Of these, 40% (355 ESTs) have been identified by similarity to sequences in public databases and classified according to functional categorization of their putative products. About 11% of the mRNAs expressed in amastigotes are related to the translational machinery, and a large number of them (9% of the total number of clones in the library) encode ribosomal proteins. A comparative analysis with a previous study, where clones from the same library were selected using sera from patients with Chagas disease, revealed that ribosomal proteins also represent the largest class of antigen coding genes expressed in amastigotes (54% of all immunoselected clones). However, although more than thirty classes of ribosomal proteins were identified by EST analysis, the results of the immunoscreening indicated that only a particular subset of them contains major antigenic determinants recognized by antibodies from Chagas disease patients.     

Analysis of expressed sequence tags of the water flea Daphnia magna.

To study gene expression in the water flea Daphnia magna we constructed a cDNA library and characterized the expressed sequence tags (ESTs) of 7210 clones. The EST sequences clustered into 2958 nonredundant groups. BLAST analyses of both protein and DNA databases showed that 1218 (41%) of the unique sequences shared significant similarities to known nucleotide or amino acid sequences, whereas the remaining 1740 (59%) showed no significant similarities to other genes. Clustering analysis revealed particularly high expression of genes related to ATP synthesis, structural proteins, and proteases. The cDNA clones and EST sequence information should be useful for future functional analysis of daphnid biology and investigation of the links between ecology and genomics.     

Clustering and analysis of protein families.

Various sequence-motif and sequence-cluster databases have been integrated into a new resource known as InterPro. Because the contributing databases have different clustering principles and scoring sensitivities, the combined assignments complement each other for grouping protein families and delineating domains. InterPro and new developments in the analysis of both the phylogenetic profiles of protein families and domain fusion events improve the prediction of specific functions for numerous proteins.     

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.     

Prediction of functional class of novel plant proteins by a statistical learning method

In plant genomes, the function of a substantial percentage of the putative protein-coding open reading frames (ORFs) is unknown. These ORFs have no significant sequence similarity to known proteins, which complicates the task of functional study of these proteins. Efforts are being made to explore methods that are complementary to, or may be used in combination with, sequence alignment and clustering methods. A web-based protein functional class prediction software, SVMProt, has shown some capability for predicting functional class of distantly related proteins. Here the usefulness of SVMProt for functional study of novel plant proteins is evaluated. To test SVMProt, 49 plant proteins (without a sequence homolog in the Swiss-Prot protein database, not in the SVMProt training set, and with functional indications provided in the literature) were selected from a comprehensive search of MEDLINE abstracts and Swiss-Prot databases in 1999-2004. These represent unique proteins the function of which, at present, cannot be confidently predicted by sequence alignment and clustering methods. The predicted functional class of 31 proteins was consistent, and that of four other proteins was weakly consistent, with published functions. Overall, the functional class of 71.4% of these proteins was consistent, or weakly consistent, with functional indications described in the literature. SVMProt shows a certain level of ability to provide useful hints about the functions of novel plant proteins with no similarity to known proteins.     

Analysis of expressed sequence tags from Brassica rapa L. ssp. pekinensis

Non-redundant expressed sequence tags (ESTs) were generated from six different organs at various developmental stages of Chinese cabbage, Brassica rapa L. ssp. pekinensis. Of the 1,295 ESTs, 915 (71%) showed significantly high homology in nucleotide or deduced amino acid sequences with other sequences deposited in databases, while 380 did not show similarity to any sequences. Briefly, 598 ESTs matched with proteins of identified biological function, 177 with hypothetical proteins or non-annotated Arabidopsis genome sequences, and 140 with other ESTs. About 82% of the top-scored matching sequences were from Arabidopsis or Brassica, but overall 558 (43%) ESTs matched with Arabidopsis ESTs at the nucleotide sequence level. This observation strongly supports the idea that gene-expression profiles of Chinese cabbage differ from that of Arabidopsis, despite their genome structures being similar to each other. Moreover, sequence analyses of 21 Brassica ESTs revealed that their primary structure is different from those of corresponding annotated sequences of Arabidopsis genes. Our data suggest that direct prediction of Brassica gene expression pattern based on the information from Arabidopsis genome research has some limitations. Thus, information obtained from the Brassica EST study is useful not only for understanding of unique developmental processes of the plant, but also for the study of Arabidopsis genome structure.     

Sequence Analysis of the 36-kb Region Between gntZ and trnY Genes of Bacillus Subtilis Genome*

Within the framework of an international Bacillus subtilis genomesequencing project, we have determined a 36-kb sequence coveringthe region between the gntZ and trnY genes. In addition to fivegenes sequenced and characterized previously, 27 putative proteincoding sequences (open reading frame; ORF) were identified.A homology search for the newly identified ORFs revealed thatsix of them had similarities to known proteins. It is notablethat new ORFs belonging to response-regulator aspartate phosphatase(Rap) and its regulator (Phr) families, and response regulatorand sensory kinase families of two-component signal transductionsystems have been identified. Furthermore, we found that some180-bp non-coding sequence, that might be an remnant of an ancientIS element, is preserved in at least five loci of the B. subtilisgenome.     

A comparison of sequence and structure protein domain families as a basis for structural genomics.

MOTIVATION: Protein families can be defined based on structure or sequence similarity. We wanted to compare two protein family databases, one based on structural and one on sequence similarity, to investigate to what extent they overlap, the similarity in definition of corresponding families, and to create a list of large protein families with unknown structure as a resource for structural genomics. We also wanted to increase the sensitivity of fold assignment by exploiting protein family HMMs. RESULTS: We compared Pfam, a protein family database based on sequence similarity, to Scop, which is based on structural similarity. We found that 70% of the Scop families exist in Pfam while 57% of the Pfam families exist in Scop. Most families that occur in both databases correspond well to each other, but in some cases they are different. Such cases highlight situations in which structure and sequence approaches differ significantly. The comparison enabled us to compile a list of the largest families that do not occur in Scop; these are suitable targets for structure prediction and determination, and may be useful to guide projects in structural genomics. It can be noted that 13 out of the 20 largest protein families without a known structure are likely transmembrane proteins. We also exploited Pfam to increase the sensitivity of detecting homologs of proteins with known structure, by comparing query sequences to Pfam HMMs that correspond to Scop families. For SWISSPROT+TREMBL, this yielded an increase in fold assignment from 31% to 42% compared to using FASTA only. This method assigned a structure to 22% of the proteins in Saccharomyces cerevisiae, 24% in Escherichia coli, and 16% in Methanococcus jannaschii.     

Identification of new RNA modifying enzymes by iterative genome search using known modifying enzymes as probes.

The complete nucleotide sequences of the Haemophilus influenzae and Mycoplasma genitalium genomes and the partially sequenced Escherichia coli chromosome were analyzed to identify open reading frames (ORFs) likely to encode RNA modifying enzymes. The protein sequences of known RNA modifying enzymes from three families--m5U methyltransferases, psi synthases and 2'-O methyltransferases--were used as probes to search sequence databases for homologs. ORFs identified as homologous to the initial probes were retrieved and used as new probes against the databases in an iterative manner until no more homologous ORFs could be identified. Using this approach, we have identified two new m5U methyltransferases, seven new psi synthases and four new 2'-O methyltransferases in E. coli. Many of the ORFs found in E.coli have direct genetic counterparts (orthologs) in one or both of H.influenzae and M.genitalium. Since there is a near-complete knowledge of RNA modifications in E.coli, functional activities of the proteins encoded by the identified ORFs were proposed based on the level of conservation of the ORFs and the modified nucleotides.     

Analysis of expressed sequence tags from the fungus Aspergillus oryzae cultured under different conditions.

We performed random sequencing of cDNAs from nine biologically or industrially important cultures of the industrially valuable fungus Aspergillus oryzae to obtain expressed sequence tags (ESTs). Consequently, 21 446 raw ESTs were accumulated and subsequently assembled to 7589 non-redundant consensus sequences (contigs). Among all contigs, 5491 (72.4%) were derived from only a particular culture. These included 4735 (62.4%) singletons, i.e. lone ESTs overlapping with no others. These data showed that consideration of culture grown under various conditions as cDNA sources enabled efficient collection of ESTs. BLAST searches against the public databases showed that 2953 (38.9%) of the EST contigs showed significant similarities to deposited sequences with known functions, 793 (10.5%) were similar to hypothetical proteins, and the remaining 3843 (50.6%) showed no significant similarity to sequences in the databases. Culture-specific contigs were extracted on the basis of the EST frequency normalized by the total number for each culture condition. In addition, contig sequences were compared with sequence sets in eukaryotic orthologous groups (KOGs), and classified into the KOG functional categories.     

Artificial neural networks trained to detect viral and phage structural proteins

Phages play critical roles in the survival and pathogenicity of their hosts, via lysogenic conversion factors, and in nutrient redistribution, via cell lysis. Analyses of phage- and viral-encoded genes in environmental samples provide insights into the physiological impact of viruses on microbial communities and human health. However, phage ORFs are extremely diverse of which over 70% of them are dissimilar to any genes with annotated functions in GenBank. Better identification of viruses would also aid in better detection and diagnosis of disease, in vaccine development, and generally in better understanding the physiological potential of any environment. In contrast to enzymes, viral structural protein function can be much more challenging to detect from sequence data because of low sequence conservation, few known conserved catalytic sites or sequence domains, and relatively limited experimental data. We have designed a method of predicting phage structural protein sequences that uses Artificial Neural Networks (ANNs). First, we trained ANNs to classify viral structural proteins using amino acid frequency; these correctly classify a large fraction of test cases with a high degree of specificity and sensitivity. Subsequently, we added estimates of protein isoelectric points as a feature to ANNs that classify specialized families of proteins, namely major capsid and tail proteins. As expected, these more specialized ANNs are more accurate than the structural ANNs. To experimentally validate the ANN predictions, several ORFs with no significant similarities to known sequences that are ANN-predicted structural proteins were examined by transmission electron microscopy. Some of these self-assembled into structures strongly resembling virion structures. Thus, our ANNs are new tools for identifying phage and potential prophage structural proteins that are difficult or impossible to detect by other bioinformatic analysis. The networks will be valuable when sequence is available but in vitro propagation of the phage may not be practical or possible.