These are not the k-mers you are looking for: efficient online k-mer counting using a probabilistic data structure

K-mer abundance analysis is widely used for many purposes in nucleotide sequence analysis, including data preprocessing for de novo assembly, repeat detection, and sequencing coverage estimation. We present the khmer software package for fast and memory efficient online counting of k-mers in sequencing data sets. Unlike previous methods based on data structures such as hash tables, suffix arrays, and trie structures, khmer relies entirely on a simple probabilistic data structure, a Count-Min Sketch. The Count-Min Sketch permits online updating and retrieval of k-mer counts in memory which is necessary to support online k-mer analysis algorithms. On sparse data sets this data structure is considerably more memory efficient than any exact data structure. In exchange, the use of a Count-Min Sketch introduces a systematic overcount for k-mers; moreover, only the counts, and not the k-mers, are stored. Here we analyze the speed, the memory usage, and the miscount rate of khmer for generating k-mer frequency distributions and retrieving k-mer counts for individual k-mers. We also compare the performance of khmer to several other k-mer counting packages, including Tallymer, Jellyfish, BFCounter, DSK, KMC, Turtle and KAnalyze. Finally, we examine the effectiveness of profiling sequencing error, k-mer abundance trimming, and digital normalization of reads in the context of high khmer false positive rates. khmer is implemented in C++ wrapped in a Python interface, offers a tested and robust API, and is freely available under the BSD license at github.com/ged-lab/khmer

Streaming and Sketch Algorithms for Large Data NLP

The availability of large and rich quantities of text data is due to the emergence of the World Wide Web, social media, and mobile devices. Such vast data sets have led to leaps in the performance of many statistically-based problems. Given a large magnitude of text data available, it is computationally prohibitive to train many complex Natural Language Processing (NLP) models on large data. This motivates the hypothesis that simple models trained on big data can outperform more complex models with small data. My dissertation provides a solution to effectively and efficiently exploit large data on many NLP applications. Datasets are growing at an exponential rate, much faster than increase in memory. To provide a memory-efficient solution for handling large datasets, this dissertation show limitations of existing streaming and sketch algorithms when applied to canonical NLP problems and proposes several new variants to overcome those shortcomings. Streaming and sketch algorithms process the large data sets in one pass and represent a large data set with a compact summary, much smaller than the full size of the input. These algorithms can easily be implemented in a distributed setting and provide a solution that is both memory- and time-efficient. However, the memory and time savings come at the expense of approximate solutions. In this dissertation, I demonstrate that approximate solutions achieved on large data are comparable to exact solutions on large data and outperform exact solutions on smaller data. I focus on many NLP problems that boil down to tracking many statistics, like storing approximate counts, computing approximate association scores like pointwise mutual information (PMI), finding frequent items (like n-grams), building streaming language models, and measuring distributional similarity. First, I introduce the concept of approximate streaming large-scale language models in NLP. Second, I present a novel variant of the Count-Min sketch that maintains approximate counts of all items. Third, I conduct a systematic study and compare many sketch algorithms that approximate count of items with focus on large-scale NLP tasks. Last, I develop fast large-scale approximate graph (FLAG), a system that quickly constructs a large-scale approximate nearest-neighbor graph from a large corpus

An Enhanced Cuckoo Search for Optimization of Bloom Filter in Spam Filtering

Bloom Filter BF is a simple but powerful data structure that can check membership to a static set The tradeoff to use Bloom filter is a certain configurable risk of false positives The odds of a false positive can be made very low if the hash bitmap is sufficiently large Spam is an irrelevant or inappropriate message sent on the internet to a large number of newsgroups or users A spam word is a list of well-known words that often appear in spam mails The proposed system of Bin Bloom Filter BBF groups the words into number of bins with different false positive rates based on the weights of the spam words An Enhanced Cuckoo Search ECS algorithm is employed to minimize the total membership invalidation cost of the BFs by finding the optimal false positive rates and number of elements stored in every bin The experimental results have demonstrated for CS and ECS for various numbers of bin