Simple sequences

Simple sequences (or microsatellites) are stretches of monotonous repetitions of short (1–5 bp) nucleotide motifs that are distributed across the whole genome in eukaryotes. They are probably generated by slippage during replication and their primary mutation rate seems to be controlled predominantly by the efficiency of the mismatch repair system. Although most mutations in simple sequence loci appear to be neutral, some mutations in particular stretches have been implicated as having a role in human genetic diseases.     

Random sequences

The comparison of protein or nucleic acid sequences frequently leads to observations whose improbability can be tested only by Monte Carlo techniques that require randomizing the sequences being compared. Two decisions need to be made. One is whether one demands a resulting random sequence to have the properties of the original sequence (a shuffled sequence) or only expects it to have them (a representative sequence). The second decision concerns the properties of the sequence of which two are composition and nearest-neighbor frequencies. It is shown that biased nearest-neighbor frequencies can significantly affect the probability of observing a given result. Methods for producing random sequences according to these decisions are given.     

Fractal genome sequences

The existence of fractal sets of DNA sequences have long been suspected on the basis of statistical analyses of genome data. In this article we identify for the first time explicitly the GA-sequences as a class of fractal genomic sequences that are easy to recognize and to extract, and are scattered densely throughout the chromosomes of a large number of genomes from different species and kingdoms including the human genome. Their existence and their fractality may have significant consequences for our understanding of the origin and evolution of genomes. Furthermore, as universal and natural markers they may be used to chart and explore the non-coding regions.     

Compilation of tRNA sequences and sequences of tRNA genes.

Sequences of 3279 sequences of tRNA genes and tRNAs published up to December 1996 are included in the compilation. Alignment of the sequences, which is most compatible with the tRNA phylogeny and known three-dimensional structures of tRNA, is used. Sequences and references are available under http://www.uni-bayreuth. de/departments/biochemie/trna/     

Matching nucleotide sequences of human antibodies with other known sequences

From an evolutionary point of view, the complementarity-determining regions of antibodies are distinct from other proteins including the framework regions of antibodies. A search for identical nucleotide sequences of eighty-four 15 consecutive bp in the complementary-determining regions of human antibody heavy chains with other known sequences yielded four matches: two sequential 15-bp matches, or one 16-bp match, with the coding region of a sea-urchin testis histone H2b-2, one 15-bp match with the promotor region of a cauliflower mosaic virus inclusion body protein, and a 15-bp match with an intron between exons 1 and 2 of human factor IX. As a control, an identical search of eighty-four 15 consecutive bp in the framework regions of human antibody heavy chains yielded no matches with other sequences except those from other antibody framework regions. Since the currently available nucleotide sequence database used in the search consisted of about 1 x 10(7) bp, finding such matches in the complementarity-determining regions might not be random.     

Finding regulatory sequences

DNA regulatory sequences control gene expression by forming DNA-protein complex with specific DNA binding protein. A major task of studies of gene regulation is to identify DNA regulatory sequences in genome-wide. Especially with the rapid pace of genome project, the function of DNA regulatory sequences becomes one of the focuses in functional genome era. Several approaches for screening and characterizing DNA regulatory sequences emerged one by one, from initial low-throughput methods to high-throughput strategies. Even though at present bioinformatics tools facilitate the process of screening regulatory fragments, the most reliable results will come from experimental test. This article highlights some experimental methods for the identification of regulatory sequences. A brief review of the history and procedures for selection methods are provided. Tendency as well as limitation and extension of these methods are also presented.     

Clustering cDNA sequences

A set of programs has been written to quantify the similaritiesbetween large numbers of cDNA sequences. This information isused to cluster similar sequences together. The main programcan cluster thousands ofcDNA sequences per day using a novel,computationally inexpensive algorithm. The clustering informationis kept in a small index file so that disk storage requirementsare negligible. Using this index file, subsidiary programs createvarious views and statistical summaries of the entire cDNA sequencecollection.     

Telomeric repeat sequences

Chromosomes not only carry transcribed genes and their regulatory DNA sequences, but also contain regions that are required for the stability and maintenace of the chromosome as a unit. These include centromeres, telomeres and origins of replication. It is clear for replication origins and centromeres that the positions of these chromosomal organelles are determined by sites of the appropriate DNA sequences, but also that functional performance requires one or more contributing proteins. Telomeres are also structurally complex, with one or more DNA components, including simple telomeric repeats and more complex telomere-associated sequences, as well as one or more specific proteins that recognize these sequences. Accumulating evidence suggests that the simple telomeric repeats are required in most, but not all species, although they are not sufficient to determine the chromosomal position of a telomere.     

Paper2sequences: retrieval of sequences listed in a publication

Our web-based tool simplifies the often laborious procedure of retrieving a set of biosequences in a publication or webpage. As a front-end to the Bioperl toolkit, it accepts as an input a list of identifiers. They are specified in an ASCII table (copy-pasted from the publication's PDF or HTML page) and give rise to queries in multiple databases for the protein/nucleic acid data specified. Currently, GenBank, PIR (Protein Information Resource) and Swiss-Prot are supported. For any sequence accession code listed, the database can be specified and, if retrieval fails, automatic lookup for the same code in other databases can be requested. Sequence length information (if specified) and heuristic rules are used to drive the lookup if multiple protein coding sequences (CDS) are part of a single accession. Warnings are issued in cases of ambiguities and inconsistencies. An advanced option enables the user to format the output in whatever format they wish.     

The study of DNA sequences by their sequences of twist angles

To study the properties of DNA sequences we have transformed the sequences of bases into the sequences of twist angles along the chain of DNA double helix by using the Dickerson sum function. The Fourier transform and the auto-correlation function of the twist angles sequences have been used to study the periodicity and randomness of the original DNA sequences. Basing on the correlation coefficient, a "distance" between two DNA fragments has been defined and used to compare some realistic DNA sequences. It is hoped that the techniques developed here could be used to analyze more realistic DNA sequences.     

Improved database searches for orthologous sequences by conditioning on outgroup sequences

MOTIVATION: Searches of biological sequence databases are usually focussed on distinguishing significant from random matches. However, the increasing abundance of related sequences on databases present a second challenge: to distinguish the evolutionarily most closely related sequences (often orthologues) from more distantly related homologues. This is particularly important when searching a database of partial sequences, where short orthologous sequences from a non-conserved region will score much more poorly than non-orthologous (outgroup) sequences from a conserved region. RESULTS: Such inferences are shown to be improved by conditioning the search results on the scores of an outgroup sequence. The log-odds score for each target sequence identified on the database has the log-odds score of the outgroup sequence subtracted from it. A test group of Caenorhabditis elegans kinase sequences and their identified C.elegans outgroups were searched against a test database of human Expressed Sequence Tag (EST) sequences, where the sets of true target sequences were known in advance. The outgroup conditioned method was shown to identify 58% more true positives ahead of the first false positive, compared to the straightforward search without an outgroup. A test dataset of 151 proteins drawn from the C.elegans genome, where the putative 'outgroup' was assigned automatically, similarly found 50% more true positives using outgroup conditioning. Thus, outgroup conditioning provides a means to improve the results of database searching with little increase in the search computation time.