The direct sequencing of PCR products generates heterozygous base-calling fluorescence chromatograms that are useful for identifying single-nucleotide polymorphisms (SNPs), insertion-deletions (indels), short tandem repeats (STRs), and paralogous genes. Indels and STRs can be easily detected using the currently available Indelligent or ShiftDetector programs, which do not search reference sequences. However, the detection of other genomic variants remains a challenge due to the lack of appropriate tools for heterozygous base-calling fluorescence chromatogram data analysis. In this study, we developed a free web-based program, Mixed Sequence Reader (MSR), which can directly analyze heterozygous base-calling fluorescence chromatogram data in .abi file format using comparisons with reference sequences. The heterozygous sequences are identified as two distinct sequences and aligned with reference sequences. Our results showed that MSR may be used to (i) physically locate indel and STR sequences and determine STR copy number by searching NCBI reference sequences; (ii) predict combinations of microsatellite patterns using the Federal Bureau of Investigation Combined DNA Index System (CODIS); (iii) determine human papilloma virus (HPV) genotypes by searching current viral databases in cases of double infections; (iv) estimate the copy number of paralogous genes, such as β-defensin 4 (DEFB4) and its paralog HSPDP3.

g Acinetobacter baumannii has emerged recently as a major cause of health care-associated infections due to the extent of its antimicrobial resistance and its propensity to cause large nosocomial outbreaks. Here we report the genome sequence of Acinetobacter baumannii TYTH-1 isolated in Taiwan during 2008.

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Exercise periodic breathing (EPB) is associated with exercise intolerance and poor prognosis in patients with heart failure (HF). However, EPB detection during cardiopulmonary exercise test (CPET) is difficult. The present study investigated the use of a wireless monitoring device to record the EPB during CPET and proposed quantization parameter estimates for the EPB. A total of 445 patients with HF were enrolled and underwent exercise tests. The ventilation data from the wearable device were compared with the data obtained during the CPET and were analyzed based on professional opinion and on 2 automated programs (decision tree [DT] and oscillatory pattern methods). The measurement accuracy was greater with the DT method (89 %) than with the oscillatory pattern method (75 %). The cutoffs for EPB recognition using the DT method were (1) an intercept of the regression line passing through the minute ventilation rate vs. the time curve during the recovery phase ≥64.63, and (2) an oscillatory phase duration to total exercise time ratio ≥0.5828. The wearable device was suitable for the assessment of EPB in patients with HF, and our new automated analysis system using the DT method effectively identified the EPB pattern.

A linear epitope prediction database (LEPD) is designed for identification of unique peptide motifs (UPMs) as specific linear epitopes for all protein families defined by Pfam. The UPMs in LEPD are extracted from each protein family by employing reinforced merging techniques that merge the primary unique patterns into a consecutive peptide based on the neighboring relationships and various levels of parameter settings. These merged peptide motifs are examined using the physicochemical and structural propensity scales for antigenic characteristics and are verified by employing background model analysis for specificity. The filtered UPMs with high antigenicity and specificity are considered as linear epitopes that provide important information for designing antibodies and vaccines. The predicted epitopes of each protein family in the LEPD can be searched in a straightforward manner, and the corresponding chemical properties be displayed in graphical and tabular formats. To verify the specificity of the predicted epitopes, each identified UPM is analyzed by scanning over the complete genomes of a series of model organisms. For any query protein possessing a resolved 3D structure, the proposed database also provides interactive visualization of the protein structures for allocation and comparison of the predicted linear epitopes. The accuracy of the prediction algorithm is evaluated to be higher than 70% in terms of mapping a UPM as a linear epitope as compared to the known databases.