Recently, I have developed a tool named MetaLogo, aimed to make sequence logos for multiple sets of sequences.
MetaLogo is a tool for making sequence logos. It can take multiple sequences as input, automatically identify the homogeneity and heterogeneity among sequences and cluster them into different groups given any wanted resolution, finally output multiple aligned sequence logos in one figure. Grouping can also be specified by users, such as grouping by lengths, grouping by sample Id, etc. Compared to conventional sequence logo generator, MetaLogo can display the total sequence population in a more detailed, dynamic and informative view.
In the auto-grouping mode, MetaLogo performs multiple sequence alignment (MSA), phylogenetic tree construction and group clustering for the input sequences. Users can give MetaLogo different resolution values to guide the sequence clustering process and the sequence logos building, which lead to a dynamic and complete understanding of the input data. In the user-defined-grouping mode, MetaLogo will perform an adjusted MSA algorithms to align multiple logos and highlight the conserved connections among groups. MetaLogo also provides a basic analysis module to present statistics of the sequences, involving sequencing characteristics distributions, conservation scores, pairwise distances, group correlations, etc. Almost all the related intermediate results are available for downloading.
Users have plenty of options to get their custom sequence logos and basic analysis figures. Multiple styles of the output are provided. Users can customize most of the elements of drawing, including shape, title, axis, ticks, labels, font color, graphic size, etc. At the same time, it can export a variety of formats including PDF, PNG, SVG and so on. It is really convenient for users without programming experiences to produce publication-ready figures.
Users could also download the standalone package of MetaLogo, integrate it into their own python project or easily set up a local MetaLogo server by using docker. A easy-to-use front website + a job queue organized back end could give users convenience to investigate and understand their sequences in their own computing environments.
2018 年注定是不平凡的一年，越来越多的区块链项目出现在视野中，越来越多的超级学术大牛也成为了各种项目的创始人、合作者、顾问。值此百家争鸣之际，我们也抽空来膜拜一下数字货币诞生初期的众多神秘的大牛们。今天我们八卦一下 Wei Dai 大佬。
Wei Dai 是谁？如果读一下比特币白皮书，会发现其中第一篇参考文献就是来自于 Wei Dai 的 b-money。大部分对比特币起源的报道文章也都会提到 b-money。b-money究竟是什么？我们引用一下以太坊白皮书里的介绍：
In 1998, Wei Dai’s b-money became the first proposal to introduce the idea of creating money through solving computational puzzles as well as decentralized consensus, but the proposal was scant on details as to how decentralized consensus could actually be implemented.
什么意思呢？翻译一下就是说，b-money 第一个提出通过解决计算机难题和达成去中心化的共识来产生电子货币。在1998年，这个理念相当创新和超前，在当时的环境下这样的系统只能部署在脑海，所以 b-money 中并未提及具体的一些共识实施细节。但这恰恰说明了 Wei Dai 的犀利之处，之后的 Hal Finney 和 Adam Back 等人的想法都与 Wei Dai 的 b-money 有关。