Utilization of the relative complexity measure to construct a phylogenetic tree for fungi

Characteristic analysis and fermentation optimization of a novel Aureobasidium pullulans RM1603 with high pullulan yield

A high-yielding microbial polysaccharide-producing strain, named RM1603, was isolated from rhizosphere soil and identified by morphological and phylogenetic analysis. The extracellular polysaccharides (EPS) were identified by thin-layer chromatography and infrared spectroscopy. The fermentation conditions were optimized by single factor experiments in shake flasks and a 5-L fermentor. The results of morphological and phylogenetic tree analysis showed that RM1603 was a strain of Aureobasidium pullulans. Its microbial polysaccharide was identified as pullulan, and the EPS production capacity reached 33.07 ± 1.03 g L-1 in shake flasks. The fermentation conditions were optimized in a 5-L fermentor, and were found to encompass an initial pH of 6.5, aeration rate of 2 vvm, rotor speed of 600 rpm, and inoculum size of 2 %. Under these conditions, the pullulan yield of RM1603 reached 62.52 ± 0.24 g L-1. Thus, this study contributes RM1603 as a new isolation with high-yielding pullulan and potential application value in biotechnology.

Production and chemical characterization of pigments in filamentous fungi

Production of pigments by filamentous fungi is gaining interest owing to their use as food colourants, in cosmetics and textiles, and because of the important biological activities of these compounds. In this context, the objectives of this study were to select pigment-producing fungi, identify these fungi based on internal transcribed spacer sequences, evaluate the growth and pigment production of the selected strains on four different media, and characterize the major coloured metabolites in their extracts. Of the selected fungal strains, eight were identified as Aspergillus sydowii (CML2967), Aspergillus aureolatus (CML2964), Aspergillus keveii (CML2968), Penicillium flavigenum (CML2965), Penicillium chermesinum (CML2966), Epicoccum nigrum (CML2971), Lecanicillium aphanocladii (CML2970) and Fusarium sp. (CML2969). Fungal pigment production was influenced by medium composition. Complex media, such as potato dextrose and malt extract, favoured increased pigment production. The coloured compounds oosporein, orevactaene and dihydrotrichodimerol were identified in extracts of L. aphanocladii (CML2970), E. nigrum (CML2971), and P. flavigenum (CML2965), respectively. These results indicate that the selected fungal strains can serve as novel sources of pigments that have important industrial applications.

GramAlign: fast alignment driven by grammar-based phylogeny

Multiple sequence alignment involves identifying related subsequences among biological sequences. When matches are found, the associated pieces are shifted so that when sequences are presented as successive rows-one sequence per row-homologous residues line-up in columns. Exact alignment of more than a few sequences is known to be computationally prohibitive. Thus many heuristic algorithms have been developed to produce good alignments in an efficient amount of time by determining an order by which pairs of sequences are progressively aligned and merged. GRAMALIGN is such a progressive alignment algorithm that uses a grammar-based relative complexity distance metric to determine the alignment order. This technique allows for a computationally efficient and scalable program useful for aligning both large numbers of sequences and sets of long sequences quickly. The GRAMALIGN software is available at http://bioinfo.unl.edu/gramalign.php for both source code download and a web-based alignment server.

Testing robustness of relative complexity measure method constructing robust phylogenetic trees for Galanthus L. using the relative complexity measure

BACKGROUND

Most phylogeny analysis methods based on molecular sequences use multiple alignment where the quality of the alignment, which is dependent on the alignment parameters, determines the accuracy of the resulting trees. Different parameter combinations chosen for the multiple alignment may result in different phylogenies. A new non-alignment based approach, Relative Complexity Measure (RCM), has been introduced to tackle this problem and proven to work in fungi and mitochondrial DNA.

RESULT

In this work, we present an application of the RCM method to reconstruct robust phylogenetic trees using sequence data for genus Galanthus obtained from different regions in Turkey. Phylogenies have been analyzed using nuclear and chloroplast DNA sequences. Results showed that, the tree obtained from nuclear ribosomal RNA gene sequences was more robust, while the tree obtained from the chloroplast DNA showed a higher degree of variation.

CONCLUSIONS

Phylogenies generated by Relative Complexity Measure were found to be robust and results of RCM were more reliable than the compared techniques. Particularly, to overcome MSA-based problems, RCM seems to be a reasonable way and a good alternative to MSA-based phylogenetic analysis. We believe our method will become a mainstream phylogeny construction method especially for the highly variable sequence families where the accuracy of the MSA heavily depends on the alignment parameters.

A grammar-based distance metric enables fast and accurate clustering of large sets of 16S sequences

Background

We propose a sequence clustering algorithm and compare the partition quality and execution time of the proposed algorithm with those of a popular existing algorithm. The proposed clustering algorithm uses a grammar-based distance metric to determine partitioning for a set of biological sequences. The algorithm performs clustering in which new sequences are compared with cluster-representative sequences to determine membership. If comparison fails to identify a suitable cluster, a new cluster is created.

Results

The performance of the proposed algorithm is validated via comparison to the popular DNA/RNA sequence clustering approach, CD-HIT-EST, and to the recently developed algorithm, UCLUST, using two different sets of 16S rDNA sequences from 2,255 genera. The proposed algorithm maintains a comparable CPU execution time with that of CD-HIT-EST which is much slower than UCLUST, and has successfully generated clusters with higher statistical accuracy than both CD-HIT-EST and UCLUST. The validation results are especially striking for large datasets.

Conclusions

We introduce a fast and accurate clustering algorithm that relies on a grammar-based sequence distance. Its statistical clustering quality is validated by clustering large datasets containing 16S rDNA sequences.

Clustering of protein families into functional subtypes using Relative Complexity Measure with reduced amino acid alphabets

Background

Phylogenetic analysis can be used to divide a protein family into subfamilies in the absence of experimental information. Most phylogenetic analysis methods utilize multiple alignment of sequences and are based on an evolutionary model. However, multiple alignment is not an automated procedure and requires human intervention to maintain alignment integrity and to produce phylogenies consistent with the functional splits in underlying sequences. To address this problem, we propose to use the alignment-free Relative Complexity Measure (RCM) combined with reduced amino acid alphabets to cluster protein families into functional subtypes purely on sequence criteria. Comparison with an alignment-based approach was also carried out to test the quality of the clustering.

Results

We demonstrate the robustness of RCM with reduced alphabets in clustering of protein sequences into families in a simulated dataset and seven well-characterized protein datasets. On protein datasets, crotonases, mandelate racemases, nucleotidyl cyclases and glycoside hydrolase family 2 were clustered into subfamilies with 100% accuracy whereas acyl transferase domains, haloacid dehalogenases, and vicinal oxygen chelates could be assigned to subfamilies with 97.2%, 96.9% and 92.2% accuracies, respectively.

Conclusions

The overall combination of methods in this paper is useful for clustering protein families into subtypes based on solely protein sequence information. The method is also flexible and computationally fast because it does not require multiple alignment of sequences.

Data Compression Concepts and Algorithms and their Applications to Bioinformatics

Data compression at its base is concerned with how information is organized in data. Understanding this organization can lead to efficient ways of representing the information and hence data compression. In this paper we review the ways in which ideas and approaches fundamental to the theory and practice of data compression have been used in the area of bioinformatics. We look at how basic theoretical ideas from data compression, such as the notions of entropy, mutual information, and complexity have been used for analyzing biological sequences in order to discover hidden patterns, infer phylogenetic relationships between organisms and study viral populations. Finally, we look at how inferred grammars for biological sequences have been used to uncover structure in biological sequences.