Discovery of Potential piRNAs from Next Generation Sequences of the Sexually Mature Porcine Testes

Piwi- interacting RNAs (piRNAs), a new class of small RNAs discovered from mammalian testes, are involved in transcriptional silencing of retrotransposons and other genetic elements in germ line cells. In order to identify a full transcriptome set of piRNAs expressed in the sexually mature porcine testes, small RNA fractions were extracted and were subjected to a Solexa deep sequencing. We cloned 6,913,561 clean reads of Sus Scrofa small RNAs (18–30 nt) and performed functional characterization. Sus Scrofa small RNAs showed a bimodal length distribution with two peaks at 21 nt and 29 nt. Then from 938,328 deep-sequenced small RNAs (26–30 nt), 375,195 piRNAs were identified by a k-mer scheme and 326 piRNAs were identified by homology searches. All piRNAs predicted by the k-mer scheme were then mapped to swine genome by Short Oligonucleotide Analysis Package (SOAP), and 81.61% of all uniquely mapping piRNAs (197,673) were located to 1124 defined genomic regions (5.85 Mb). Within these regions, 536 and 501 piRNA clusters generally distributed across only minus or plus genomic strand, 48 piRNA clusters distributed on two strands but in a divergent manner, and 39 piRNA clusters distributed on two strands in an overlapping manner. Furthermore, expression pattern of 7 piRNAs identified by homology searches showed 5 piRNAs displayed a ubiquitous expression pattern, although 2 piRNAs were specifically expressed in the testes. Overall, our results provide new information of porcine piRNAs and their specific expression pattern in porcine testes suggests that piRNAs have a role in regulating spermatogenesis

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Reference-Based Super-Resolution Method for Remote Sensing Images with Feature Compression Module

High-quality remote sensing images play important roles in the development of ecological indicators’ mapping, urban-rural management, urban planning, and other fields. Compared with natural images, remote sensing images have more abundant land cover along with lower spatial resolutions. Given the embedded longitude and latitude information of remote sensing images, reference (Ref) images with similar scenes could be more accessible. However, existing traditional super-resolution (SR) approaches always depend on increases in network depth to improve performance, which limits the acquisition and application of high-quality remote sensing images. In this paper, we proposed a novel, reference-image-based, super-resolution method with feature compression module (FCSR) for remote sensing images to alleviate the above issue while effectively utilizing high-resolution (HR) information from Ref images. Specifically, we exploited a feature compression branch (FCB) to extract relevant features in feature detail matching with large measurements. This branch employed a feature compression module (FCM) to extract features from low-resolution (LR) and Ref images, which enabled texture transfer from different perspectives. To decrease the impact of environmental factors such as resolution, brightness and ambiguity disparities between the LR and Ref images, we designed a feature extraction encoder (FEE) to ensure accuracy in feature extraction in the feature acquisition branch. The experimental results demonstrate that the proposed FCSR achieves significant performance and visual quality compared to state-of-the-art SR methods. Explicitly, when compared with the best method, the average peak signal-to-noise ratio (PSNR) index on the three test sets is improved by 1.0877%, 0.8161%, 1.0296%, respectively, and the structural similarity (SSIM) index on four test sets is improved by 1.4764%, 1.4467%, 0.0882%, and 1.8371%, respectively. Simultaneously, FCSR obtains satisfactory visual details following qualitative evaluation

Characterization of <i>sus scrofa</i> small RNAs.

<p>(A) Length distribution of small RNAs. <i>Sus scrofa</i> small RNAs displayed a bimodal length distribution with two peaks at 21 nt and 29 nt. (B) Bar chart summarizing the annotation of small RNA populations in total RNA from testes. (C) Chromosomal distribution of small RNAs.</p

Seven piRNAs expression profile in the porcine testes.

<p>(A) Seven piRNAs expression profile in the porcine testes from 60-day and 180-day testes of Chinese Meishan and Large White pigs using stem-loop qRT-PCR method. The X-axis represents the small RNAs and the Y-axis shows the relative expression levels of small RNAs (–ΔC_t values for qRT-PCR). The significance of differences for the expression between 60-day (sexually immature) and 180-day (sexually mature) testes of Chinese Meishan and Large White pigs was calculated using two-tailed T-test. *, p≤0.05; **, p≤0.01. (B) Seven piRNAs expressed in the porcine testes by stem-loop semi-quantitative RT-PCR. M, 25 bp DNA ladder; He, heart; Li, liver; Sp, spleen; Lu, lung; Ki, kidney; LM, <i>longissimu</i>s <i>dorsi</i> muscle; BF, backfat; Te, testis; Ov, ovary.</p

The chromosomal distribution of piRNAs and piRNA clusters in the genome.

*<p>All piRNAs with the unique perfectly mapping to pig genome were used to analyze to chromosome distribution. MT, mitochondrial genome.</p