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.

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

Example Input: Background: The HIV-1 RNA genome has a biased nucleotide composition with a surplus of As. Several hypotheses have been put forward to explain this striking phenomenon, but the A-count of the HIV-1 genome has thus far not been systematically manipulated. The reason for this reservation is the likelihood that known and unknown sequence motifs will be affected by such a massive mutational approach, thus resulting in replication-impaired virus mutants. We present the first attempt to increase and decrease the A-count in a relatively small polymerase (pol) gene segment of HIV-1 RNA. To minimize the mutational impact, a new mutational approach was developed that is inspired by natural sequence variation as present in HIV-1 isolates. This phylogeny-instructed mutagenesis allowed us to create replication-competent HIV-1 mutants with a significantly increased or decreased local A-count. The local A-count of the wild-type (wt) virus (40.2%) was further increased to 46.9% or reduced to 31.7 and 26.3%. These HIV-1 variants replicate efficiently in vitro, despite the fact that the pol changes cause a quite profound move in HIV-SIV sequence space. Extrapolating these results to the complete 9 kb RNA genome, we may cautiously suggest that the A-rich signature does not have to be maintained. This survey also provided clues that silent codon changes, in particular from G-to-A, determine the subtype-specific sequence signatures.
Example Output: HIV-1 tolerates changes in A-count in a small segment of the pol gene

Example Input: Motivation: Deep sequencing of clinical samples is now an established tool for the detection of infectious pathogens, with direct medical applications. The large amount of data generated produces an opportunity to detect species even at very low levels, provided that computational tools can effectively profile the relevant metagenomic communities. Data interpretation is complicated by the fact that short sequencing reads can match multiple organisms and by the lack of completeness of existing databases, in particular for viral pathogens. Here we present metaMix, a Bayesian mixture model framework for resolving complex metagenomic mixtures. We show that the use of parallel Monte Carlo Markov chains for the exploration of the species space enables the identification of the set of species most likely to contribute to the mixture. Results: We demonstrate the greater accuracy of metaMix compared with relevant methods, particularly for profiling complex communities consisting of several related species. We designed metaMix specifically for the analysis of deep transcriptome sequencing datasets, with a focus on viral pathogen detection; however, the principles are generally applicable to all types of metagenomic mixtures. Availability and implementation: metaMix is implemented as a user friendly R package, freely available on CRAN: http://cran.r-project.org/web/packages/metaMix Contact: sofia.morfopoulou.10@ucl.ac.uk Supplementary information: Supplementary data are available at Bionformatics online.
Example Output:
Bayesian mixture analysis for metagenomic community profiling