The process of brain aging is intricate, encompassing significant structural and functional changes, including myelination and iron deposition in the brain. Brain age could act as a quantitative marker to evaluate the degree of the individual's brain evolution. Quantitative susceptibility mapping (QSM) is sensitive to variations in magnetically responsive substances such as iron and myelin, making it a favorable tool for estimating brain age. In this study, we introduce an innovative 3D convolutional network named Segmentation-Transformer-Age-Network (STAN) to predict brain age based on QSM data. STAN employs a two-stage network architecture. The first-stage network learns to extract informative features from the QSM data through segmentation training, while the second-stage network predicts brain age by integrating the global and local features. We collected QSM images from 712 healthy participants, with 548 for training and 164 for testing. The results demonstrate that the proposed method achieved a high accuracy brain age prediction with a mean absolute error (MAE) of 4.124 years and a coefficient of determination (R2) of 0.933. Furthermore, the gaps between the predicted brain age and the chronological age of Parkinson's disease patients were significantly higher than those of healthy subjects (P<0.01). We thus believe that using QSM-based predicted brain age offers a more reliable and accurate phenotype, with the potentiality to serve as a biomarker to explore the process of advanced brain aging.

BACKGROUND: DNA barcoding technology, which uses a short piece of DNA sequence to identify species, has wide ranges of applications. Until today, a universal DNA barcode marker for plants remains elusive. The rbcL and matK regions have been proposed as the "core barcode" for plants and the ITS2 and psbA-trnH intergenic spacer (PTIGS) regions were later added as supplemental barcodes. The use of PTIGS region as a supplemental barcode has been limited by the lack of computational tools that can handle significant insertions and deletions in the PTIGS sequences. Here, we compared the most commonly used alignment-based and alignment-free methods and developed a web server to allow the biologists to carry out PTIGS-based DNA barcoding analyses. RESULTS: First, we compared several alignment-based methods such as BLAST and those calculating P distance and Edit distance, alignment-free methods Di-Nucleotide Frequency Profile (DNFP) and their combinations. We found that the DNFP and Edit-distance methods increased the identification success rate to ~80%, 20% higher than the most commonly used BLAST method. Second, the combined methods showed overall better success rate and performance. Last, we have developed a web server that allows (1) retrieving various sub-regions and the consensus sequences of PTIGS, (2) annotating novel PTIGS sequences, (3) determining species identity by PTIGS sequences using eight methods, and (4) examining identification efficiency and performance of the eight methods for various taxonomy groups. CONCLUSIONS: The Edit distance and the DNFP methods have the highest discrimination powers. Hybrid methods can be used to achieve significant improvement in performance. These methods can be extended to applications using the core barcodes and the other supplemental DNA barcode ITS2. To our knowledge, the web server developed here is the only one that allows species determination based on PTIGS sequences. The web server can be accessed at http://psba-trnh-plantidit.dnsalias.org.

BACKGROUND: Small RNAs are known to regulate diverse gene expression processes including translation, transcription, and splicing. Among small RNAs, the microRNAs (miRNAs) of 17 to 27 nucleotides (nts) undergo biogeneses including primary transcription, RNA excision and folding, nuclear export, cytoplasmic processing, and then bioactivity as regulatory agents. We propose that analogous hairpins from RNA molecules that function as part of the spliceosome might also be the source of small, regulatory RNAs (somewhat smaller than miRNAs). RESULTS: Deep sequencing technology has enabled discovery of a novel 16-nt RNA sequence in total RNA from human brain that we propose is derived from RNU1, an RNA component of spliceosome assembly. Bioinformatic alignments compel inquiring whether the novel 16-nt sequence or its precursor have a regulatory function as well as determining aspects of how processing intersects with the miRNA biogenesis pathway. Specifically, our preliminary in silico investigations reveal the sequence could regulate splicing factor Arg/Ser rich 1 (SFRS1), a gene coding an essential protein component of the spliceosome. All 16-base source sequences in the UCSC Human Genome Browser are within the 14 instances of RNU1 genes listed in wgEncodeGencodeAutoV3. Furthermore, 10 of the 14 instances of the sequence are also within a common 28-nt hairpin-forming subsequence of RNU1. CONCLUSIONS: An abundant 16-nt RNA sequence is sourced from a spliceosomal RNA, lies in a stem of a predicted RNA hairpin, and includes reverse complements of subsequences of the 3'UTR of a gene coding for a spliceosome protein. Thus RNU1 could function both as a component of spliceosome assembly and as inhibitor of production of the essential, spliceosome protein coded by SFRS1. Beyond this example, a general procedure is needed for systematic discovery of multiple alignments of sequencing, splicing, and RNA folding data.

SUMMARY: Many genomic and proteomic analyses generate as a result a tree of genes or proteins. These trees are often large (containing tens of thousands of nodes and edges), and need a visualization tool to fully display all the information contained in the tree. Clustering analysis can be performed on these trees to obtain clusters of proteins, and we need an efficient way to visualize the clustering results. We present a novel tree visualization tool to help with such analyses. AVAILABILITY: http://www2.renci.org/~jeff/software/bin/win32/ProteinVis-2.1.6-win32.zip.

MOTIVATION: Reductions in genotyping costs have heightened interest in performing whole genome association scans and in the fine mapping of candidate regions. Improvements in study design and analytic techniques will require the simulation of datasets with realistic patterns of linkage disequilibrium and allele frequencies for typed SNPs. METHODS: We describe a general approach to simulate genotyped datasets for standard case-control or affected child trio data, by resampling from existing phased datasets. The approach allows for considerable flexibility in disease models, potentially involving a large number of interacting loci. The method is most applicable for diseases caused by common variants that have not been under strong selection, a class specifically targeted by the International HapMap project. RESULTS: Using the three population Phase I/II HapMap data as a testbed for our approach, we have implemented the approach in HAP-SAMPLE, a web-based simulation tool.

Molecular sequences, like all experimental data, are subject to error. Many current DNA sequencing protocols have very significant error rates and often generate artefactual insertions and deletions of bases (indels) which corrupt the translation of sequences and compromise the detection of protein homologies. The impact of these errors on the utility of molecular sequence data is dependent on the analytic technique used to interpret the data. In the presence of frameshift errors, standard algorithms using six-frame translation can miss important homologies because only subfragments of the correct translation are available in any given frame. We present a new algorithm which can detect and correct frameshift errors in DNA sequences during comparison of translated sequences with protein sequences in the databases. This algorithm can recognize homologous proteins sharing 30% identity even in the presence of a 7% frameshift error rate. Our algorithm uses dynamic programming, producing a guaranteed optimal alignment in the presence of frameshifts, and has a sensitivity equivalent to Smith-Waterman. The computational efficiency of the algorithm is O(nm) where n and m are the sizes of two sequences being compared. The algorithm does not rely on prior knowledge or heuristic rules and performs significantly better than any previously reported method.

The authors present a parallel merging algorithm that, on an exclusive-read exclusive-write (EREW) parallel random-access machine (PRAM) with k processors merges two sorted lists of total length n in O(n/k+log n) time and constant extra space per processor, and hence is time-space optimal for any value of k>