New Lead-free Hybrid Layered Double Perovskite Halides: Synthesis, Structural Transition and Ultralow Thermal Conductivity.

Hybrid layered double perovskites (HLDPs), representing the two-dimensional manifestation of halide double perovskites, have elicited considerable interest owing to their intricate chemical bonding hierarchy and structural diversity. This intensified interest stems from the diverse options available for selecting alternating octahedral coordinated trivalent [M(III)] and monovalent metal centers [M(I)], along with the distinctive nature of the cationic organic amine located between the layers. Here, we have synthesized three new compounds with general formula (R'/R'')4/2M(III)M(I)Cl8; where R'=C3H7NH3 (i.e. 3N) and R''=NH3C4H8NH3 (i.e. 4N4); M(III)=In3+ or Ru3+; M(I)=Cu+ by simple solution-based acid precipitation method. The structural analysis reveals that (4N4)2CuInCl8 and (4N4)2CuRuCl8 adopt the layered Dion Jacobson (DJ) structure, whereas (3N)4CuInCl8 exhibits layered Ruddlesden Popper (RP) structure. The alternative octahedra within the inorganic layer display distortions and tilting. Three compounds show temperature-dependent structural phase transitions where changes in the staking of inorganic layer, extent of octahedral tilting and reorientation of organic spacers with temperature have been noticed. We have achieved ultralow lattice thermal conductivity (κL) in the HLDPs in the 2 to 300 K range, marking a distinctive feature within the realm of HLDP systems. The RP-HLDP compound, (3N)4CuInCl8, demonstrates anisotropy in κL while measured parallel and perpendicular to layer stacking, showcasing ultralow κL of 0.15 Wm-1K-1 at room temperature, which is one of the lowest values obtained among Pb-free metal halide perovskite. The observed ultralow κL in three new HLDPs is attributed to significant lattice anharmonicity arising from the chemical bonding heterogeneity and soft crystal structure, which resulted in low-energy localized optical phonon modes that suppress heat-carrying acoustic phonons.

A hybrid and scalable error correction algorithm for indel and substitution errors of long reads.

BACKGROUND: Long-read sequencing has shown the promises to overcome the short length limitations of second-generation sequencing by providing more complete assembly. However, the computation of the long sequencing reads is challenged by their higher error rates (e.g., 13% vs. 1%) and higher cost ($0.3 vs. $0.03 per Mbp) compared to the short reads. METHODS: In this paper, we present a new hybrid error correction tool, called ParLECH (Parallel Long-read Error Correction using Hybrid methodology). The error correction algorithm of ParLECH is distributed in nature and efficiently utilizes the k-mer coverage information of high throughput Illumina short-read sequences to rectify the PacBio long-read sequences.ParLECH first constructs a de Bruijn graph from the short reads, and then replaces the indel error regions of the long reads with their corresponding widest path (or maximum min-coverage path) in the short read-based de Bruijn graph. ParLECH then utilizes the k-mer coverage information of the short reads to divide each long read into a sequence of low and high coverage regions, followed by a majority voting to rectify each substituted error base. RESULTS: ParLECH outperforms latest state-of-the-art hybrid error correction methods on real PacBio datasets. Our experimental evaluation results demonstrate that ParLECH can correct large-scale real-world datasets in an accurate and scalable manner. ParLECH can correct the indel errors of human genome PacBio long reads (312 GB) with Illumina short reads (452 GB) in less than 29 h using 128 compute nodes. ParLECH can align more than 92% bases of an E. coli PacBio dataset with the reference genome, proving its accuracy. CONCLUSION: ParLECH can scale to over terabytes of sequencing data using hundreds of computing nodes. The proposed hybrid error correction methodology is novel and rectifies both indel and substitution errors present in the original long reads or newly introduced by the short reads.

Large-scale parallel genome assembler over cloud computing environment.

The size of high throughput DNA sequencing data has already reached the terabyte scale. To manage this huge volume of data, many downstream sequencing applications started using locality-based computing over different cloud infrastructures to take advantage of elastic (pay as you go) resources at a lower cost. However, the locality-based programming model (e.g. MapReduce) is relatively new. Consequently, developing scalable data-intensive bioinformatics applications using this model and understanding the hardware environment that these applications require for good performance, both require further research. In this paper, we present a de Bruijn graph oriented Parallel Giraph-based Genome Assembler (GiGA), as well as the hardware platform required for its optimal performance. GiGA uses the power of Hadoop (MapReduce) and Giraph (large-scale graph analysis) to achieve high scalability over hundreds of compute nodes by collocating the computation and data. GiGA achieves significantly higher scalability with competitive assembly quality compared to contemporary parallel assemblers (e.g. ABySS and Contrail) over traditional HPC cluster. Moreover, we show that the performance of GiGA is significantly improved by using an SSD-based private cloud infrastructure over traditional HPC cluster. We observe that the performance of GiGA on 256 cores of this SSD-based cloud infrastructure closely matches that of 512 cores of traditional HPC cluster.