In this task, you're given a paragraph and title from the research paper. Your task is to classify whether the given title is suitable or not for the research paper based on the given paragraph. Return "True" if title is proper according to paragraph else "False".

[Q]: Paragraph: The A1 ribosomal frameshifting requires the existence of an in cis RNA slippery sequence and is promoted by a downstream stimulator RNA. An atypical RNA pseudoknot with an extra stem formed by complementary sequences within loop 2 of an H-type pseudoknot is characterized in the severe acute respiratory syndrome coronavirus (SARS CoV) genome. This pseudoknot can serve as an efficient stimulator for A1 frameshifting in vitro. Mutational analysis of the extra stem suggests frameshift efficiency can be modulated via manipulation of the secondary structure within the loop 2 of an infectious bronchitis virus-type pseudoknot. More importantly, an upstream RNA sequence separated by a linker 5 0 to the slippery site is also identified to be capable of modulating the A1 frameshift efficiency. RNA sequence containing this attenuation element can downregulate A1 frameshifting promoted by an atypical pseudoknot of SARS CoV and two other pseudoknot stimulators. Furthermore, frameshift efficiency can be reduced to half in the presence of the attenuation signal in vivo. Therefore, this in cis RNA attenuator represents a novel negative determinant of general importance for the regulation of A1 frameshift efficiency, and is thus a potential antiviral target.
Title: An atypical RNA pseudoknot stimulator and an upstream attenuation signal for A1 ribosomal frameshifting of SARS coronavirus
[A]: False

[Q]: Paragraph: Background 3 In silico bacterial, viral, and human truth datasets were generated to evaluate available metagenomics 4 algorithms. Sequenced datasets include background organisms, creating ambiguity in the true 5 source organism for each read. Bacterial and viral datasets were created with even and staggered 6 coverage to evaluate organism identification, read mapping, and gene identification capabilities of 7 available algorithms. These truth datasets are provided as a resource for the development and 8 refinement of metagenomic algorithms. Algorithm performance on these truth datasets can inform 9 decision makers on strengths and weaknesses of available algorithms and how the results may be best 10 leveraged for bacterial and viral organism identification and characterization. 11 12 Source organisms were selected to mirror communities described in the Human Microbiome Project as 13 well as the emerging pathogens listed by the National Institute of Allergy and Infectious Diseases. The 14 six in silico datasets were used to evaluate the performance of six leading metagenomics algorithms: 15 MetaScope, Kraken, LMAT, MetaPhlAn, MetaCV, and MetaPhyler. 16 Results Algorithms were evaluated on runtime, true positive organisms identified to the genus and species 19 levels, false positive organisms identified to genus and species level, read mapping, relative abundance 20 estimation, and gene calling. No algorithm out performed the others in all categories, and the 21 algorithm or algorithms of choice strongly depends on analysis goals. MetaPhlAn excels for bacteria 22 and LMAT for viruses. The algorithms were ranked by overall performance using a normalized 23 weighted sum of the above metrics, and MetaScope emerged as the overall winner, followed by Kraken 24 and LMAT. 25 Conclusions 27 Simulated FASTQ datasets with well--characterized truth data about microbial community composition 28 reveal numerous insights about the relative strengths and weaknesses of the metagenomics algorithms 29 evaluated. The simulated datasets are available to download from the Sequence Read Archive 30 (SRP062063). 31 32
Title: Evaluating performance of metagenomic characterization algorithms using in silico datasets generated with FASTQSim
[A]: False

[Q]: Paragraph: Sequence analysis revealed that rice dwarf phytoreovirus segment S12 is 1066 nucleotides long with a small out-ofphase, overlapping open reading frame (ORF) as well as a major ORF. The large ORF (positions 42 to 960) encodes 312 amino acids, while the small one (bases 313 to 591) encodes 92 amino acids with an additional in-frame AUG codon (positions 337-339) 24 nucleotides downstream from the first one. Transcripts from a full-length cDNA directed the in vitro synthesis of three polypeptides of 33 (considered to be translated from the long ORF), 8, and 7 kDa. Alteration of each of the two ATG codons on the small ORF demonstrated their involvement in the generation of the 8-and 7-kDa polypeptides. Although it is still unknown whether these proteins are expressed in t&o, the small ORF is shown to be conserved in S9s of two other members of the genus Phytoreovirus, rice gall dwarf virus and wound tumor virus, suggesting its common, important function.
Title: Rice Dwarf Phytoreovirus Segment S12 Transcript Is Tricistronic in Vim
[A]:
False