%유전체 서열 분석의 대부분은 유사한 서열 데이터를 찾아 비교 분석을 통해 점진적으로 진행되므로, 대용량의 서열 데이터를 빠르게 비교하고 탐색하는 기술이 필요하다.
Most genome sequence analyses proceed through comparative analysis by finding similar sequence data. Therefore, there is a need for a technique to quickly compare and search for large amounts of sequence data.
%Alignment 기법은 매우 정확도가 높은 서열 비교 방법이지만, 시간, 공간 복잡도가 높아 대용량의 서열을 처리하기에 부적합하다.
The alignment technique is a very accurate method to compare sequences, but its high time and space complexity is inadequate to handle large sequences.
%이와 같은 문제점을 극복하기 위해, 우리는 random plot 기반의 서열 비교 및 탐색 모델을 제안하였다.
To overcome these disadvantage, we suggest a new method for comparison and finding for Mega size sequence.
%유전체 서열을 3차원 상의 random walk으로 변환하여, 서열 비교 문제를 geometric object 비교 문제로 치환하였다.
Converts the genome sequence as a random plot on the three-dimensional, followed by replacing the sequence comparison problem with geometric object comparison problem.
%실험결과, 제안하는 비교 모델이 제시한 유사도 결과값은 원본 서열과 변형된 서열 사이의 변형 비율을 정확히 반영하였다.
As a result of experiments, similarity precessed by our comparison model accurately reflects the modified ratio between the modified sequence and the original sequence.
Most analytical studies based on visualization derive only a single result because they derive a numerical value based on the final result of the visualization.
The search and comparison method based on the sequence visualization proposed in this study has high value of utilization of information because all compressed partial visualization information is used for searching sequence.
It is useful in that the partial similarity of the sequence can be measured.
%또 1-10M 크기의 쿼리 서열을 200M 이상의 whole genome 서열에서 탐색한 결과, 원본 서열 뿐만 아니라 20\%까지 변형된 서열에 대해서도 비교적 정확한 위치를 찾아냈다.
In addition, a query sequence of size 1-10M was searched in a whole genome sequence of 200M or more, and a relatively precise position was found for the original sequence as well as the modified sequence up to 20\%.
%아울러 탐색 시간도 25초 내외로서, alignment 알고리즘에 비해 매우 개선된 속도로 처리됨을 확인할 수 있었다.
Also the search time 25 seconds or less, was confirmed handled in a very improved speed compared to the alignment algorithm.


% ---------- 새로 추가된 부분 ----------
%반면 LTR 서열과 같이 kilobyte 단위의 길이가 짧은 서열을 쿼리로 사용할 경우, 시각화 할 때 압축률이 낮아지고, 이는 결과적으로 레퍼런스 서열의 압축률을 낮추게 되어 상대적으로 탐색시간이 길어졌다.
On the other hand, when a sequence with a shorter kilobyte unit length is used as a query, such as an LTR sequence, the compression rate is lowered at the time of visualization, resulting in a lower compression rate of the reference sequence, which leads to a longer search time.
%그러나 레퍼런스의 길이를 감안했을 때, 탐색된 위치의 오차는 비교적 정확함을 확인할 수 있었다.
However, considering the length of the reference, we can confirm that the position searched is relatively accurate.

The proposed alignment-free searching method is very fast and effective to find a long query sequence over the whole genomes whose size is more than multi-hundreds mega-bytes.
%본 연구에서 제시한 모델은 서열 데이터를 3차원 상의 random plot object로 변형하여 압축된 정보로 유사도를 비교함으로써, alignment 기반의 모델보다 대폭 개선된 속도로 서열의 비교 및 탐색을 수행할 수 있었다.
It was able to compare and search the sequence at a much improved rate than the alignment-based model by modifying the sequence data into a three-dimensional random plot object and comparing the similarity with the compressed information.
Searching algorithm based on alignment method is popular and works good biological sequence comparison but if the size of query and target reference is very large (more than 100 mega bases) the alignment base algorithm requires huge memory space and takes a long computation time.
Though our algorithm can't locates the position of query sequence exactly by the DNA base unit, but we can use this procedure as one preprocessing step to find query sequence. 