Quantification of the variation in percentage identity for protein sequence alignments

BACKGROUND: Percentage Identity (PID) is frequently quoted in discussion of sequence alignments since it appears simple and easy to understand. However, although there are several different ways to calculate percentage identity and each may yield a different result for the same alignment, the method of calculation is rarely reported. Accordingly, quantification of the variation in PID caused by the different calculations would help in interpreting PID values in the literature. In this study, the variation in PID was quantified systematically on a reference set of 1028 alignments generated by comparison of the protein three-dimensional structures. Since the alignment algorithm may also affect the range of PID, this study also considered the effect of algorithm, and the combination of algorithm and PID method. RESULTS: The maximum variation in PID due to the calculation method was 11.5% while the effect of alignment algorithm on PID was up to 14.6% across three popular alignment methods. The combined effect of alignment algorithm and PID calculation gave a variation of up to 22% on the test data, with an average of 5.3% ± 2.8% for sequence pairs with < 30% identity. In order to see which PID method was most highly correlated with structural similarity, four different PID calculations were compared to similarity scores (Sc) from the comparison of the corresponding protein three-dimensional structures. The highest correlation coefficient for a PID calculation was 0.80. In contrast, the more sophisticated Z-score calculated by reference to randomized sequences gave a correlation coefficient of 0.84. CONCLUSION: Although it is well known amongst expert sequence analysts that PID is a poor score for discriminating between protein sequences, the apparent simplicity of the percentage identity score encourages its widespread use in establishing cutoffs for structural similarity. This paper illustrates that not only is PID a poor measure of sequence similarity when compared to the Z-score, but that there is also a large uncertainty in reported PID values. Since better alternatives to PID exist to quantify sequence similarity, these should be quoted where possible in preference to PID. The findings presented here should prove helpful to those new to sequence analysis, and in warning those who seek to interpret the value of a PID reported in the literature

Cleaved end-face quality of microstructured polymer optical fibres

The cutting of a microstructured polymer optical fibre to form an optical end-face is studied. The effect of the temperature and speed of the cutting blade on the end-face is qualitatively assessed and it is found that for fibres at temperatures in the range 70–90 C, a blade at a similar temperature moving at a speed of less than 0.5 mm/s produces a good quality end-face. The nature of the damage caused by the cutting process was examined and found to vary with fibre temperature, blade quality and cut depth. Thermo-mechanical analysis showed that the drawn material was significantly more visco-elastic than the annealed raw material in the 70-90 C temperature range. The behaviour of the surface damage with cut depth was found to be consistent with the behaviour of a visco-elastic material

GOtcha: a new method for prediction of protein function assessed by the annotation of seven genomes

BACKGROUND: The function of a novel gene product is typically predicted by transitive assignment of annotation from similar sequences. We describe a novel method, GOtcha, for predicting gene product function by annotation with Gene Ontology (GO) terms. GOtcha predicts GO term associations with term-specific probability (P-score) measures of confidence. Term-specific probabilities are a novel feature of GOtcha and allow the identification of conflicts or uncertainty in annotation. RESULTS: The GOtcha method was applied to the recently sequenced genome for Plasmodium falciparum and six other genomes. GOtcha was compared quantitatively for retrieval of assigned GO terms against direct transitive assignment from the highest scoring annotated BLAST search hit (TOPBLAST). GOtcha exploits information deep into the 'twilight zone' of similarity search matches, making use of much information that is otherwise discarded by more simplistic approaches. At a P-score cutoff of 50%, GOtcha provided 60% better recovery of annotation terms and 20% higher selectivity than annotation with TOPBLAST at an E-value cutoff of 10(-4). CONCLUSIONS: The GOtcha method is a useful tool for genome annotators. It has identified both errors and omissions in the original Plasmodium falciparum annotation and is being adopted by many other genome sequencing projects

Correction:A study of the structural properties of sites modified by the O-linked 6-N-acetylglucosamine transferase

[This corrects the article DOI: 10.1371/journal.pone.0184405.]

SNAPPI-DB: a database and API of Structures, iNterfaces and Alignments for Protein–Protein Interactions

SNAPPI-DB, a high performance database of Structures, iNterfaces and Alignments of Protein–Protein Interactions, and its associated Java Application Programming Interface (API) is described. SNAPPI-DB contains structural data, down to the level of atom co-ordinates, for each structure in the Protein Data Bank (PDB) together with associated data including SCOP, CATH, Pfam, SWISSPROT, InterPro, GO terms, Protein Quaternary Structures (PQS) and secondary structure information. Domain–domain interactions are stored for multiple domain definitions and are classified by their Superfamily/Family pair and interaction interface. Each set of classified domain–domain interactions has an associated multiple structure alignment for each partner. The API facilitates data access via PDB entries, domains and domain–domain interactions. Rapid development, fast database access and the ability to perform advanced queries without the requirement for complex SQL statements are provided via an object oriented database and the Java Data Objects (JDO) API. SNAPPI-DB contains many features which are not available in other databases of structural protein–protein interactions. It has been applied in three studies on the properties of protein–protein interactions and is currently being employed to train a protein–protein interaction predictor and a functional residue predictor. The database, API and manual are available for download at:

AlmostSignificant:simplifying quality control of high-throughput sequencing data

Motivation: The current generation of DNA sequencing technologies produce a large amount of data quickly. All of these data need to pass some form of quality control (QC) processing and checking before they can be used for any analysis. The large number of samples that are run through Illumina sequencing machines makes the process of QC an onerous and time-consuming task that requires multiple pieces of information from several sources. Results: AlmostSignificant is an open-source platform for aggregating multiple sources of quality metrics as well as run and sample meta-data associated with DNA sequencing runs from Illumina sequencing machines. AlmostSignificant is a graphical platform to streamline the QC of DNA sequencing data, to store these data for future reference together with extra meta-data associated with the sequencing runs not typically retained. This simplifies the challenge of monitoring the volume of data produced by Illumina sequencers. AlmostSignificant has been used to track the quality of over 80 sequencing runs covering over 2500 samples produced over the last three years. Availability and Implementation: The code and documentation for AlmostSignificant is freely available at https://github.com/bartongroup/AlmostSignificant. Contacts: [email protected] or [email protected] Supplementary information: Supplementary data are available at Bioinformatics online

Nanopore direct RNA sequencing maps the complexity of Arabidopsis mRNA processing and m6A modification

Understanding genome organization and gene regulation requires insight into RNA transcription, processing and modification. We adapted nanopore direct RNA sequencing to examine RNA from a wild-type accession of the model plant Arabidopsis thaliana and a mutant defective in mRNA methylation (m6A). Here we show that m6A can be mapped in full-length mRNAs transcriptome-wide and reveal the combinatorial diversity of cap-associated transcription start sites, splicing events, poly(A) site choice and poly(A) tail length. Loss of m6A from 3’ untranslated regions is associated with decreased relative transcript abundance and defective RNA 30 end formation. A functional consequence of disrupted m6A is a lengthening of the circadian period. We conclude that nanopore direct RNA sequencing can reveal the complexity of mRNA processing and modification in full-length single molecule reads. These findings can refine Arabidopsis genome annotation. Further, applying this approach to less well-studied species could transform our understanding of what their genomes encode