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It was shown that Seq and Mc values increase depending on the nature of CLs in the following order: ECH>MBAM>POCl3.
In Figure 2(a), it is clear that SEQ correctly identifies all catalytic residues, which are located on the globally most rigid (small SEQ values) regions.
The results show that SEQ performs even better than POOL T + G + C) when sequence conservation is added (thick solid line in Figure 6).
It is somewhat interesting that SEQ, which only uses structural rigidity, has comparable results as those of POOL, which uses residue biochemical features, evolutionary sequence conservation, and cleft shape.
The prediction results show that SEQ and STR are much better features than B-factor (MCC = 0.47 for SEQ and STR, 0.25 for B-factor) for identification of catalytic residues.
Similar(55)
Thus, we applied the proposed procedure to an RNA-seq data, given that RNA-seq technology has perfect precision and sensitivity to detect low-expressed genes.
These features are absent from standard RNA-Seq data, and indicate that Nascent-Seq predominantly detects nascent RNA molecules attached to elongating Pol II.
Using the sub-sampled INPUT-seq data, we show that INPUT-seq generally provides higher genomic coverage at sequencing depth as low as one million reads.
Moreover, Ribo-Zero-Seq and DSN-Seq have consistent transcript quantification using FFPE RNAs, suggesting that RNA-Seq can be used with FFPE-derived RNAs for gene expression profiling.
An apparent advantage of RNA-seq-based versus microarray-based transcriptome analysis is that RNA-seq can provide transcript structure information.
In addition, we found that RNA-Seq data analysis for both PH-1 and ebr1 RNA-Seq is very reproducible.
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