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: Streptococcus pneumoniae is the main cause of diseases such as meningitis, pneumoniae and sepsis, especially in children and old people. Due to costly antibiotic treatment, and increasing resistance of pneumococcus, developing high-efficient protective vaccine against this pathogen is an urgent need. Although the pneumoniae polysaccharide vaccine (PPV) and pneumonia conjugate vaccines (PCV) are the efficient pneumococcal vaccine in children and adult groups, but the serotype replacement of S. pneumoniae strains causes the reduction in efficacy of such vaccines. For overcoming the aforesaid drawbacks epitope-based vaccines are introduced as the relevant alternative. In our previous research, the epitope vaccine was designed based on immunodominant epitopes from PspA, CbpA antigens as cellular stimulants and PhtD, PiuA as humoral stimulants. Because the low immunogenicity is the main disadvantage of epitope vaccine, in the current study, we applied coiled-coil selfassembled structures for developing our vaccine. Recently, self-assembled peptide nanoparticles (SAPNs) have gained much attention in the field of vaccine development due to their multivalency, self-adjuvanticity, biocompatibility, and size similarity to pathogen. In this regard, the final designed vaccine is comprised of cytotoxic T lymphocytes (CTL) epitopes from PspA and CbpA, helper T lymphocytes (HTL) epitopes from PhtD and PiuA, the pentamer and trimmer oligomeric domains form 5-stranded and 3-stranded coiled-coils as self-assembled scaffold, Diphtheria toxoids (DTD) as a universal T-helper, which fused to each other with appropriate linkers. The four different arrangements based on the order of above-mentioned compartments were constructed, and each of them were modeled, and validated to find the 3D structure. The structural, physicochemical, and immunoinformatics analyses of final vaccine construct represented that our vaccine could stimulate potent immune response against S. pneumoniae; however, the potency of that should be approved via various in vivo and in vitro immunological tests.
Example Output: Designing self-assembled peptide nanovaccine against Streptococcus pneumoniae: An in silico strategy

Example Input: Overlapping genes represent a fascinating evolutionary puzzle, since they encode two functionally unrelated proteins from the same DNA sequence. They originate by a mechanism of overprinting, in which point mutations in an existing frame allow the expression (the "birth") of a completely new protein from a second frame. In viruses, in which overlapping genes are abundant, these new proteins often play a critical role in infection, yet they are frequently overlooked during genome annotation. This results in erroneous interpretation of mutational studies and in a significant waste of resources. Therefore, overlapping genes need to be correctly detected, especially since they are now thought to be abundant also in eukaryotes. Developing better detection methods and conducting systematic evolutionary studies require a large, reliable benchmark dataset of known cases. We thus assembled a highquality dataset of 80 viral overlapping genes whose expression is experimentally proven. Many of them were not present in databases. We found that overall, overlapping genes differ significantly from non-overlapping genes in their nucleotide and amino acid composition. In particular, the proteins they encode are enriched in high-degeneracy amino acids and depleted in low-degeneracy ones, which may alleviate the evolutionary constraints acting on overlapping genes. Principal component analysis revealed that the vast majority of overlapping genes follow a similar composition bias, despite their heterogeneity in length and function. Six proven mammalian overlapping genes also followed this bias. We propose that this apparently near-universal composition bias may either favour the birth of overlapping genes, or/and result from selection pressure acting on them.
Example Output: Overlapping genes and the proteins they encode differ significantly in their sequence composition from non-overlapping genes

Example Input: The very rapid growth in molecular sequence data from the daily accretion of large gene and protein sequencing projects have led to issues regarding viewing and analyzing the massive amounts of data. Graphical representation and numerical characterization of DNA, RNA and protein sequences have exhibited great potential to address these concerns. We review here in brief several different formulations of these representations and examples of applications to diverse problems based on what this author had presented at the Second Mathematical Chemistry Workshop of the Americas in Bogota, Colombia in 2010. In particular, we note several insights that were gained from such representations, and the applications to the bio-medicinal field.
Example Output:
The GRANCH Techniques for Analysis of DNA, RNA and Protein Sequences