BACKGROUND: The sequencing of immunoglobulin (Ig) transcripts from single B cells yields essential information about Ig heavy:light chain pairing, which is lost in conventional bulk sequencing experiments. The previously limited throughput of single-cell approaches has recently been overcome by the introduction of multiple next-generation sequencing (NGS)-based platforms. Furthermore, single-cell techniques allow the assignment of additional data types (e.g. cell surface marker expression), which are crucial for biological interpretation. However, the currently available computational tools are not designed to handle single-cell data and do not provide integral solutions for linking of sequence data to other biological data. RESULTS: Here we introduce sciReptor, a flexible toolkit for the processing and analysis of antigen receptor repertoire sequencing data at single-cell level. The software combines bioinformatics tools for immunoglobulin sequence annotation with a relational database, where raw data and analysis results are stored and linked. sciReptor supports attribution of additional data categories such as cell surface marker expression or immunological metadata. Furthermore, it comprises a quality control module as well as basic repertoire visualization tools. CONCLUSION: sciReptor is a flexible framework for standardized sequence analysis of antigen receptor repertoires on single-cell level. The relational database allows easy data sharing and downstream analyses as well as immediate comparisons between different data sets.

The Otto Warburg International Summer School and Workshop 2005, held in Berlin, Germany, focussed on the topic of "Networks and Regulation", which is a very active field of research these days. The lecturers at the school were asked to give tutorial introductions that would allow the students to follow a research talk presented towards the end of the workshop. Overall, these lectures presented material starting where the text books stop and bridged to the current research front. The lectures were very well received by the students, prompting the lecturers to jointly work on a volume introducing the current research in this area.

MOTIVATION: Neighbor-dependent substitution processes generated specific pattern of dinucleotide frequencies in the genomes of most organisms. The CpG-methylation-deamination process is, e.g. a prominent process in vertebrates (CpG effect). Such processes, often with unknown mechanistic origins, need to be incorporated into realistic models of nucleotide substitutions. RESULTS: Based on a general framework of nucleotide substitutions we developed a method that is able to identify the most relevant neighbor-dependent substitution processes, estimate their relative frequencies and judge their importance in order to be included into the modeling. Starting from a model for neighbor independent nucleotide substitution we successively added neighbor-dependent substitution processes in the order of their ability to increase the likelihood of the model describing given data. The analysis of neighbor-dependent nucleotide substitutions based on repetitive elements found in the genomes of human, zebrafish and fruit fly is presented. AVAILABILITY: A web server to perform the presented analysis is freely available at: http://evogen.molgen.mpg.de/server/substitution-analysis

MOTIVATION: Substantial regional variations of substitutional processes have recently been reported from human/mouse comparisons. However, several features including the C + G dependence and the CpG-based transition effect remain obscure. RESULTS: Utilizing the vast amount of transposable elements in the human genome, we performed detailed analysis of the substitutional and insertion/deletion patterns along the human lineage in a regional and time-resolved fashion. We observed a drastic increase in the CpG-based transition frequency at about the time of the mammalian radiation. We also observed clear regional biases of substitution patterns, most notably a bias to enrich the C+G content toward the telomeres. AVAILABILITY: The programs used are available upon request from the authors.

We introduce a model of DNA sequence evolution which can account for biases in mutation rates that depend on the identity of the neighboring bases. An analytic solution for this class of models is developed by adopting well-known methods of nonlinear dynamics. Results are presented for the CpG-methylation-deamination process which dominates point substitutions in vertebrates. The dinucleotide frequencies generated by the model (using empirically obtained mutation rates) match the overall pattern observed in non-coding DNA. A web-based tool has been constructed to compute single- and dinucleotide frequencies for arbitrary neighbor-dependent mutation rates. Also provided is the backward procedure to infer the mutation rates using maximum likelihood analysis given the observed single- and dinucleotide frequencies. Reasonable estimates of the mutation rates can be obtained very efficiently, using generic non-coding DNA sequences as input, after masking out long homonucleotide subsequences. Our method is much more convenient and versatile to use than the traditional method of deducing mutation rates by counting mutation events in carefully chosen sequences. More generally, our approach provides a more realistic but still tractable description of non-coding genomic DNA, and may be used as a null model for various sequence analysis applications.