In this task, you're given a paragraph from the research paper and your task is to generate a suitable title for the research paper based on the given paper. Under 100 words is a good title length.

Q: Background: We present here the recent update of AMS algorithm for identification of post-translational modification (PTM) sites in proteins based only on sequence information, using artificial neural network (ANN) method. The query protein sequence is dissected into overlapping short sequence segments. Ten different physicochemical features describe each amino acid; therefore nine residues long segment is represented as a point in a 90 dimensional space. The database of sequence segments with confirmed by experiments post-translational modification sites are used for training a set of ANNs. The efficiency of the classification for each type of modification and the prediction power of the method is estimated here using recall (sensitivity), precision values, the area under receiver operating characteristic (ROC) curves and leave-one-out tests (LOOCV). The significant differences in the performance for differently optimized neural networks are observed, yet the AMS 3.0 tool integrates those heterogeneous classification schemes into the single consensus scheme, and it is able to boost the precision and recall values independent of a PTM type in comparison with the currently available state-of-the art methods. The standalone version of AMS 3.0 presents an efficient way to indentify post-translational modifications for whole proteomes. The training datasets, precompiled binaries for AMS 3.0 tool and the source code are available at Additional file 1 Supplementary materials for the publication. Supplementary.xls contains detailed results of training and testing of AMS 3.0 tool in MS Excel file format. Additional file 2 PTM predictor software with training data. PTM_Archive.tar.gz includes the predictor software binaries together with all trained ANNs in tar.gz compressed form.

A: AMS 3.0: prediction of post-translational modifications
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Q: Fifty-five volunteers treated with either intranasal recombinant interferon (rIFN; 2 x 10 6 IV/day) or placebo for 15 days were exposed to coronavirus by direct intranasal inoculation on the eighth day of treatment. Symptom scores were recorded, and cultures of virus were taken daily for all volunteers for seven days after inoculation. Nineteen (73%) of the 26 placebo recipients met symptom-score criteria for a cold, compared with 12 (41070) of the IFN recipients (P = .02). The mean nasal symptom scores in the placebo and IFN groups were 9.2 and 5.4, respectively (P = .03), and the mean total symptom scores in the two groups were 23.2 and 9.4, respectively (P = .003). The mean number of days with a total symptom score >4 was 1.6 in the placebo recipients and 0.5 in the rIFN recipients (P = .02). Prophylactic intranasal rIFN effectively shortened the duration and reduced the severity of coronavirus cold symptoms.

A: Prevention of Experimental Coronavirus Colds with Intranasal (l-2b Interferon
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Q: Next generation sequencing (NGS) is a powerful tool for the characterization, discovery, and molecular identification of RNA viruses. There were multiple NGS library preparation methods published for strand-specific RNA-seq, but some methods are not suitable for identifying and characterizing RNA viruses. In this study, we report a NGS library preparation method to identify RNA viruses using the Ion Torrent PGM platform. The NGS sequencing adapters were directly inserted into the sequencing library through reverse transcription and polymerase chain reaction, without fragmentation and ligation of nucleic acids. The results show that this method is simple to perform, able to identify multiple species of RNA viruses in clinical samples.

A:
Next-generation sequencing library preparation method for identification of RNA viruses on the Ion Torrent Sequencing Platform
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