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.

Ex Input:
The development of high-throughput molecular technologies and associated bioinformatics has dramatically changed the capacities of scientists to produce, handle, and analyze large amounts of genomic, transcriptomic, and proteomic data. A clear example of this step-change is represented by the amount of DNA sequence data that can be now produced using next-generation sequencing (NGS) platforms. Similarly, recent improvements in protein and peptide separation effi ciencies and highly accurate mass spectrometry have promoted the identifi cation and quantifi cation of proteins in a given sample. These advancements in biotechnology have increasingly been applied to the study of animal infectious diseases and are beginning to revolutionize the way that biological and evolutionary processes can be studied at the molecular level. Studies have demonstrated the value of NGS technologies for molecular characterization, ranging from metagenomic characterization of unknown pathogens or microbial communities to molecular epidemiology and evolution of viral quasispecies. Moreover, high-throughput technologies now allow detailed studies of hostpathogen interactions at the level of their genomes (genomics), transcriptomes (transcriptomics), or proteomes (proteomics). Ultimately, the interaction between pathogen and host biological networks can be questioned by analytically integrating these levels (integrative OMICS and systems biology). The application of high-throughput biotechnology platforms in these fi elds and their typical low-cost per information content has revolutionized the resolution with which these processes can now be studied. The aim of this chapter is to provide a current and prospective view on the opportunities and challenges associated with the application of massive parallel sequencing technologies to veterinary medicine, with particular focus on applications that have a potential impact on disease control and management.

Ex Output:
Chapter 30 Next-Generation Sequencing in Veterinary Medicine: How Can the Massive Amount of Information Arising from High- Throughput Technologies Improve Diagnosis, Control, and Management of Infectious Diseases?

Ex Input:
Sixteen longSAGE libraries from four different clinical stages of cervical intraepithelial neoplasia have enabled us to identify novel cell-surface biomarkers indicative of CIN stage. By comparing gene expression profi les of cervical tissue at early and advanced stages of CIN, several genes are identifi ed to be novel genetic markers. We present fi fty-six cell-surface gene products differentially expressed during progression of CIN. These cell surface proteins are being examined to establish their capacity for optical contrast agent binding. Contrast agent visualization will allow real-time assessment of the physiological state of the disease process bringing vast benefi t to cancer care. The data discussed in this publication have been submitted to NCBIs Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) and are accessible through GEO Series accession number GSE6252.

Ex Output:
Using LongSAGE to Detect Biomarkers of Cervical Cancer Potentially Amenable to Optical Contrast Agent Labelling

Ex Input:
Streptococcus mutans is a bacterial cause of dental caries that is resistant to bacitracin. The aim of this study was to elucidate the mbrABCD-related bacitracin resistance mechanism of S. mutans. Transcriptome data demonstrated that the expression levels of 33 genes were induced more than twofold by bacitracin. Fourteen genes were selected from the upregulated genes, and defective mutants of these genes were constructed for measurement of their sensitivity to bacitracin. Among the mutants, only the mbrA-or mbrB-deficient mutants exhibited 100-to 121-fold greater sensitivity to bacitracin when compared with the wild-type strain. Moreover, knockout of the mbrC and mbrD genes abolished the bacitracin-induced mbrAB upregulation. These results suggest that both mbrC and mbrD are required for mbrAB upregulation that confers the bacitracinresistant phenotype on S. mutans.

Ex Output:
Bacitracin Upregulates mbrAB Transcription via mbrCD to Confer Bacitracin Resistance in Streptococcus mutans