A T3 and T7 Recombinant Phage Acquires Efficient Adsorption and a Broader Host Range

It is usually thought that bacteriophage T7 is female specific, while phage T3 can propagate on male and female Escherichia coli. We found that the growth patterns of phages T7M and T3 do not match the above characteristics, instead showing strain dependent male exclusion. Furthermore, a T3/7 hybrid phage exhibits a broader host range relative to that of T3, T7, as well as T7M, and is able to overcome the male exclusion. The T7M sequence closely resembles that of T3. T3/7 is essentially T3 based, but a DNA fragment containing part of the tail fiber gene 17 is replaced by the T7 sequence. T3 displays inferior adsorption to strains tested herein compared to T7. The T3 and T7 recombinant phage carries altered tail fibers and acquires better adsorption efficiency than T3. How phages T3 and T7 recombine was previously unclear. This study is the first to show that recombination can occur accurately within only 8 base-pair homology, where four-way junction structures are identified. Genomic recombination models based on endonuclease I cleavages at equivalent and nonequivalent sites followed by strand annealing are proposed. Retention of pseudo-palindromes can increase recombination frequency for reviving under stress

The Isolation of Nucleic Acids from Fixed, Paraffin-Embedded Tissues–Which Methods Are Useful When?

Museums and pathology collections around the world represent an archive of genetic material to study populations and diseases. For preservation purposes, a large portion of these collections has been fixed in formalin-containing solutions, a treatment that results in cross-linking of biomolecules. Cross-linking not only complicates isolation of nucleic acid but also introduces polymerase “blocks” during PCR. A wide variety of methods exists for the recovery of DNA and RNA from archival tissues, and although a number of previous studies have qualitatively compared the relative merits of the different techniques, very few have undertaken wide scale quantitative comparisons. To help address this issue, we have undertaken a study that investigates the quality of nucleic acids recovered from a test panel of fixed specimens that have been manipulated following a number of the published protocols. These include methods of pre-treating the samples prior to extraction, extraction and nucleic acid purification methods themselves, and a post-extraction enzymatic repair technique. We find that although many of the published methods have distinct positive effects on some characteristics of the nucleic acids, the benefits often come at a cost. In addition, a number of the previously published techniques appear to have no effect at all. Our findings recommend that the extraction methodology adopted should be chosen carefully. Here we provide a quick reference table that can be used to determine appropriate protocols for particular aims

The chlL ( frxC ) gene: Phylogenetic distribution in vascular plants and DNA sequence from Polystichum acrostichoides ( Pteridophyta ) and Synechococcus sp. 7002 ( Cyanobacteria )

We examined chlL ( frxC ) gene evolution using several approaches. Sequences from the chloroplast genome of the fern Polystichum acrostichoides and from the cyanobacterium Synechococcus sp. 7002 were determined and found to be highly conserved. A complete physical map of the fern chloroplast genome and partial maps of other vascular plant taxa show that chlL is located primarily in the small single copy region as in Marchantia polymorpha. A survey of a wide variety of non-angiospermous vascular plant DNAs shows that chlL is widely distributed but has been lost in the pteridophyte Psilotum and (presumably independently) within the Gnetalean gymnosperms.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/41636/1/606_2004_Article_BF00994092.pd

Intelligent mining of large-scale bio-data: bioinformatics applications

Today, there is a collection of a tremendous amount of bio-data because of the computerized applications worldwide. Therefore, scholars have been encouraged to develop effective methods to extract the hidden knowledge in these data. Consequently, a challenging and valuable area for research in artificial intelligence has been created. Bioinformatics creates heuristic approaches and complex algorithms using artificial intelligence and information technology in order to solve biological problems. Intelligent implication of the data can accelerate biological knowledge discovery. Data mining, as biology intelligence, attempts to find reliable, new, useful and meaningful patterns in huge amounts of data. Hence, there is a high potential to raise the interaction between artificial intelligence and bio-data mining. The present paper argues how artificial intelligence can assist bio-data analysis and gives an up-to-date review of different applications of bio-data mining. It also highlights some future perspectives of data mining in bioinformatics that can inspire further developments of data mining instruments. Important and new techniques are critically discussed for intelligent knowledge discovery of different types of row datasets with applicable examples in human, plant and animal sciences. Finally, a broad perception of this hot topic in data science is given

Highly Specific Protein Sequence Motifs for Genome Analysis

We present a novel method for discovering conserved sequence motifs from families of aligned protein sequences. The method has been implemented as a computer program called EMOTIF (http://motif.stanford.edu/emotif). Given an aligned set of protein sequences, EMOTIF generates a set of motifs with a wide range of specificities and sensitivities. EMOTIF can also generate motifs that describe possible subfamilies of a protein superfamily. A disjunction of such motifs can often represent the entire superfamily with high specificity and sensitivity. We have used EMOTIF to generate sets of motifs from all 7,000 protein alignments in the BLOCKS and PRINTS databases. The resulting database, called IDENTIFY (http://motif.stanford.edu/identify), contains over 50,000 motifs. For each alignment, the database contains several motifs having a probability of matching a false positive that range from 10 -10 to 10 -5 . Highly specific motifs are well suited for searching entire proteomes, while gen..